1) Data Processing

NOTE: This includes all the set-up and processing for the CLSA Comprehensive cohort data

1.1) Set-up

if (!require("pacman")) install.packages("pacman")
## Loading required package: pacman
pacman::p_load(MASS,ggplot2,lme4,nlme, rms,dplyr, lubridate, effects,
               lmerTest,rpart,tableone,psych,Hmisc,magrittr, ggeffects,sjmisc,splines,
               lsmeans,openxlsx)

set.seed(1)

setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data")

emm_options(rg.limit = 350000)

1.2) Tracking Cohort

1.2.1) Baseline processing

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Baseline (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_BL")

trackBL<-read.csv("23SP001_McMaster_PRaina_Baseline_Trav4.csv")#Comprehensive Cohort Baseline

track.BL<-trackBL[c(1:5,104,926,798,106,892,921,32,1363,1377,1378,1379,1380,1407,1408,1433,1466,1432,1465,
                    1480,1481:1488,893,928,1093,1094,1096,1125,1124,376,1086,1089,1097:1102,1105,1106,
                    1129,1132,1133,1077,1076,1075,1078,1079,1085,1082,1130,1087,1090,1088,1131,1091,1080,
                    1128,1095,1081,334)]

#Sex
track.BL$Sex<-track.BL$SEX_ASK_TRM

#Age
track.BL$Age<-track.BL$AGE_NMBR_TRM

#Marital Status
track.BL$Relationship_status<-NA
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==1]<-"Single"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==2]<-"Married"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==3]<-"Widowed"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==4]<-"Divorced"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==5]<-"Separated"

#Education 4 Category
track.BL$Education4<-NA
track.BL$Education4[track.BL$ED_UDR04_TRM==1]<-"Less than High School Diploma"
track.BL$Education4[track.BL$ED_UDR04_TRM==2]<-"High School Diploma"
track.BL$Education4[track.BL$ED_UDR04_TRM==3]<-"Some College"
track.BL$Education4[track.BL$ED_UDR04_TRM==4]<-"College Degree or Higher"


#Household Income
track.BL$Income_Level<-NA
track.BL$Income_Level[track.BL$INC_PTOT_TRM==1]<-"<$20k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==2]<-"$20-50k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==3]<-"$50-100k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==4]<-"$100-150k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==5]<-">$150k"

#Living Status
track.BL$Living_status<-NA
track.BL$Living_status[track.BL$OWN_DWLG_TRM==1]<-"House"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==2 |track.BL$OWN_DWLG_TRM==6]<-"Apartment/Condo/Townhome"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==3]<-"Assisted Living"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==4 | track.BL$OWN_DWLG_TRM==5 | track.BL$OWN_DWLG_TRM>=7]<-"Other"

#Alcohol      
track.BL$Alcohol<-NA
track.BL$Alcohol[track.BL$ALC_TTM_TRM==1]<-"Regular drinker (at least once a month)"
track.BL$Alcohol[track.BL$ALC_TTM_TRM==2]<-"Occasional drinker"
track.BL$Alcohol[track.BL$ALC_TTM_TRM==3]<-"Non-drinker"

#Smoking Status
track.BL$Smoking_Status<-NA
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==1]<-"Daily Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==2 |track.BL$SMK_DSTY_TRM==3]<-"Occasional Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==4 | track.BL$SMK_DSTY_TRM==5]<-"Former Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==6]<-"Never Smoked"

track.BL$Ethnicity<-NA
track.BL$Ethnicity[track.BL$SDC_CULT_WH_TRM==1]<-"White"
track.BL$Ethnicity[track.BL$SDC_CULT_WH_TRM==0]<-"Other"

#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.BL$PASE_Q1<-NA
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==1]<-0
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==2]<-0.11
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==3]<-0.25
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==4]<-0.43
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ>4]<-NA
track.BL$PASE_Q1[is.na(track.BL$PA2_SIT_MCQ)]<-NA

track.BL$PASE_Q1B<-NA
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==1]<-0
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==2]<-1
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==3]<-3
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==4]<-6
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==5]<-10
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ>5]<- 0
track.BL$PASE_Q1B[is.na(track.BL$PA2_SITHR_MCQ)]<-NA
track.BL$PASE_Q1B<-as.numeric(track.BL$PASE_Q1B)

#Q2: Walking outside frequency
track.BL$PASE_Q2<- NULL
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==1]<-0
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==2]<-0.11
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==3]<-0.25
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==4]<-0.43
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ>4]<-NA
track.BL$PASE_Q2[is.na(track.BL$PA2_WALK_MCQ)]<-NA

track.BL$PASE_Q2A<- NA
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==1]<-0
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==2]<-1
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==3]<-3
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==4]<-6
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==5]<-10
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ>5]<-NA
track.BL$PASE_Q2A[is.na(track.BL$PA2_WALKHR_MCQ)]<-NA
track.BL$PASE_Q2A<-as.numeric(track.BL$PASE_Q2A)


#Q3: Light Sports or Activity Frequency
track.BL$PASE_Q3<- NA
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==1]<-0
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==2]<-0.11
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==3]<-0.25
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==4]<-0.43
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ>4]<-NA
track.BL$PASE_Q3[is.na(track.BL$PA2_LSPRT_MCQ)]<-NA

track.BL$PASE_Q3A<- NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==1]<-0
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==2]<-1
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==3]<-3
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==4]<-6
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==5]<-10
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRT_MCQ == 1 | is.na(track.BL$PA2_LSPRTHR_MCQ)]<-0
track.BL$PASE_Q3A[is.na(track.BL$PA2_LSPRT_MCQ) & is.na(track.BL$PA2_LSPRTHR_MCQ)]<-NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRT_MCQ>4 & is.na(track.BL$PA2_LSPRTHR_MCQ)]<-NA
track.BL$PASE_Q3A<-as.numeric(track.BL$PASE_Q3A)


#Q4: Moderate Sports or Activity Frequency
track.BL$PASE_Q4<-NA
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==1]<-0
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==2]<-0.11
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==3]<-0.25
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==4]<-0.43
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ>4]<-NA
track.BL$PASE_Q4[is.na(track.BL$PA2_MSPRT_MCQ)]<-NA

track.BL$PASE_Q4A<- NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==1]<-0
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==2]<-1
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==3]<-3
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==4]<-6
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==5]<-10
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRT_MCQ == 1 | is.na(track.BL$PA2_MSPRTHR_MCQ)]<-0
track.BL$PASE_Q4A[is.na(track.BL$PA2_MSPRT_MCQ) & is.na(track.BL$PA2_MSPRTHR_MCQ)]<-NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRT_MCQ>4 & is.na(track.BL$PA2_MSPRTHR_MCQ)]<-NA
track.BL$PASE_Q4A<-as.numeric(track.BL$PASE_Q4A)


#Q5: Strenuous Sports or Activity Frequency
track.BL$PASE_Q5<-NA
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==1]<-0
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==2]<-0.11
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==3]<-0.25
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==4]<-0.43
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ>4]<-NA
track.BL$PASE_Q5[is.na(track.BL$PA2_SSPRT_MCQ)]<-NA

track.BL$PASE_Q5A<- NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==1]<-0
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==2]<-1
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==3]<-3
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==4]<-6
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==5]<-10
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRT_MCQ == 1 | is.na(track.BL$PA2_SSPRTHR_MCQ)]<-0
track.BL$PASE_Q5A[is.na(track.BL$PA2_SSPRT_MCQ) & is.na(track.BL$PA2_SSPRTHR_MCQ)]<-NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRT_MCQ>4 & is.na(track.BL$PA2_SSPRTHR_MCQ)]<-NA
track.BL$PASE_Q5A<-as.numeric(track.BL$PASE_Q5A)


#Q6: Muscle strengthening and endurance exercise
track.BL$PASE_Q6<-NA
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==1]<-0
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==2]<-0.11
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==3]<-0.25
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==4]<-0.43
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ>4]<-NA
track.BL$PASE_Q6[is.na(track.BL$PA2_EXER_MCQ)]<-NA

track.BL$PASE_Q6A<- NA
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==1]<-0
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==2]<-1
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==3]<-3
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==4]<-6
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==5]<-10
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ>5]<-NA
track.BL$PASE_Q6A[track.BL$PA2_EXER_MCQ == 1 | is.na(track.BL$PA2_EXERHR_MCQ)]<-0
track.BL$PASE_Q6A[is.na(track.BL$PA2_EXER_MCQ) & is.na(track.BL$PA2_EXERHR_MCQ)]<-NA
track.BL$PASE_Q6A[track.BL$PA2_EXER_MCQ>4 & is.na(track.BL$PA2_EXERHR_MCQ)]<-NA
track.BL$PASE_Q6A<-as.numeric(track.BL$PASE_Q6A)


#Q7: Light Housework
track.BL$PASE_Q7<-NA
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ==1]<-1
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ==2]<-0
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ>2]<-NA
track.BL$PASE_Q7[is.na(track.BL$PA2_LTHSWK_MCQ)]<-NA
track.BL$PASE_Q7<-as.numeric(track.BL$PASE_Q7)


#Q8: Heavy Housework
track.BL$PASE_Q8<-NA
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ==1]<-1
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ==2]<-0
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ>2]<-NA
track.BL$PASE_Q8[is.na(track.BL$PA2_HVYHSWK_MCQ)]<-NA
track.BL$PASE_Q8<-as.numeric(track.BL$PASE_Q8)


#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.BL$PASE_Q9A<-NA
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ==1]<-1
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ==2]<-0
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ>2]<-NA
track.BL$PASE_Q9A[is.na(track.BL$PA2_HMREPAIR_MCQ)]<-NA
track.BL$PASE_Q9A<-as.numeric(track.BL$PASE_Q9A)

track.BL$PASE_Q9B<-NA
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ==1]<-1
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ==2]<-0
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ>2]<-NA
track.BL$PASE_Q9B[is.na(track.BL$PA2_HVYODA_MCQ)]<-NA
track.BL$PASE_Q9B<-as.numeric(track.BL$PASE_Q9B)

track.BL$PASE_Q9C<-NA
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ==1]<-1
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ==2]<-0
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ>2]<-NA
track.BL$PASE_Q9C[is.na(track.BL$PA2_LTODA_MCQ)]<-NA
track.BL$PASE_Q9C<-as.numeric(track.BL$PASE_Q9C)

track.BL$PASE_Q9D<-NA
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ==1]<-1
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ==2]<-0
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ>2]<-NA
track.BL$PASE_Q9D[is.na(track.BL$PA2_CRPRSN_MCQ)]<-NA
track.BL$PASE_Q9D<-as.numeric(track.BL$PASE_Q9D)

#Q10: Working and Volunteering
track.BL$PASE_Q10<-NA
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ==1]<-1
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ==2]<-0
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ>2]<-NA
track.BL$PASE_Q10[is.na(track.BL$PA2_WRK_MCQ)]<-NA
track.BL$PASE_Q10<-as.numeric(track.BL$PASE_Q10)

track.BL$PASE_Q10A<-NA
track.BL$PASE_Q10A<-track.BL$PA2_WRKHRS_NB_MCQ
track.BL$PASE_Q10A[track.BL$PA2_WRKHRS_NB_MCQ>=700]<-NA
track.BL$PASE_Q10A[track.BL$PA2_WRK_MCQ == 2 | is.na(track.BL$PA2_WRKHRS_NB_MCQ)]<-0
track.BL$PASE_Q10A[is.na(track.BL$PA2_WRK_MCQ) & is.na(track.BL$PA2_WRKHRS_NB_MCQ)]<-NA
track.BL$PASE_Q10A<-as.numeric(track.BL$PASE_Q10A)
track.BL$PASE_Q10A<-track.BL$PASE_Q10A/7

#PASE TOTAL SCORE#
track.BL$PASE_TOTAL<-track.BL$PASE_Q2*track.BL$PASE_Q2A*20 + track.BL$PASE_Q3*track.BL$PASE_Q3A*21 +  track.BL$PASE_Q4*track.BL$PASE_Q4A*23 + track.BL$PASE_Q5*track.BL$PASE_Q5A*30 +
  track.BL$PASE_Q6*track.BL$PASE_Q6A*30 + (track.BL$PASE_Q7+track.BL$PASE_Q8)*25 + track.BL$PASE_Q9A*30 + track.BL$PASE_Q9B*36 + track.BL$PASE_Q9C*20 + track.BL$PASE_Q9D*35 + 
  track.BL$PASE_Q10*track.BL$PASE_Q10A*21


#BMI
track.BL$BMI<-track.BL$HWT_DBMI_TRM
track.BL$BMI[track.BL$HWT_DBMI_TRM>100]<-NA

#CESD-10
track.BL$CESD_10<-track.BL$DEP_CESD10_TRM
track.BL$CESD_10[track.BL$DEP_CESD10_TRM>50]<-NA
track.BL$CESD_10[track.BL$DEP_CESD10_TRM==-88]<-NA

#Subjective Cognitive Impairment
track.BL$SCI<- NA
track.BL$SCI[track.BL$CCT_MEMPB_TRM==1]<- "Yes"
track.BL$SCI[track.BL$CCT_MEMPB_TRM==2]<- "No"

#Dementia and AD
track.BL$Dementia<- NA
track.BL$Dementia[track.BL$CCT_ALZH_TRM==1]<- "Yes"
track.BL$Dementia[track.BL$CCT_ALZH_TRM==2]<- "No"

#Anxiety
track.BL$Anxiety<- NA
track.BL$Anxiety[track.BL$CCT_ANXI_TRM==1]<- "Yes"
track.BL$Anxiety[track.BL$CCT_ANXI_TRM==2]<- "No"

#Mood Disorders
track.BL$Mood_Disord<- NA
track.BL$Mood_Disord[track.BL$CCT_MOOD_TRM==1]<- "Yes"
track.BL$Mood_Disord[track.BL$CCT_MOOD_TRM==2]<- "No"

#Pet Ownership at Baseline
track.BL$Pet_Owner<-NA
track.BL$Pet_Owner[track.BL$SSA_PET_TRM==1]<-"Yes"
track.BL$Pet_Owner[track.BL$SSA_PET_TRM==2]<-"No"

#Number of Chronic Conditions
track.BL$Chronic_conditions<-NA

track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==1]<- 1 #Heart Disease
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==2]<- 0
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==8]<- NA
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==9]<- NA

track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==1]<- 1 #peripheral vascular disease
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==2]<- 0
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==8]<- NA
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==9]<- NA

track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==1]<- 1 #SCI
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==2]<- 0
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==8]<- NA
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==9]<- NA

track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==1]<- 1 #Alzheimers or demeinta
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==2]<- 0
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==8]<- NA
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==9]<- NA

track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==1]<- 1 #Multiple sclerosis
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==2]<- 0
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==8]<- NA
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==9]<- NA

track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==1]<- 1 #Epilepsy
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==2]<- 0
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==8]<- NA
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==9]<- NA

track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==1]<- 1 #Migraine headaches
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==2]<- 0
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==8]<- NA
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==9]<- NA

track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==1]<- 1 #Intenstinal or stomach ulcers
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==2]<- 0
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==8]<- NA
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==9]<- NA

track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==1]<- 1 #Bowel disorder
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==2]<- 0
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==8]<- NA
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==9]<- NA

track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==1]<- 1 #Bowel incontinence
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==2]<- 0
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==8]<- NA
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==9]<- NA

track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==1]<- 1 #Urinary incontinence
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==2]<- 0
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==8]<- NA
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==9]<- NA

track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==1]<- 1 #Macular degeneration
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==2]<- 0
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==8]<- NA
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==9]<- NA

track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==1]<- 1 #All-cause cancer
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==2]<- 0
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==8]<- NA
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==9]<- NA

track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==1]<- 1 #Back problems but not fibromyalgia or arthritis
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==2]<- 0
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==8]<- NA
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==9]<- NA

track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==1]<- 1 #Kidney disease
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==2]<- 0
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==8]<- NA
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==9]<- NA

track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==1]<- 1 #Other long term mental or physical condition
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==2]<- 0
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==8]<- NA
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==9]<- NA

track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==1]<- 1 #Hand arthritis
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==2]<- 0
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==8]<- NA
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==9]<- NA

track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==1]<- 1 #Hip arthritis
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==2]<- 0
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==8]<- NA
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==9]<- NA

track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==1]<- 1 #Knee arthritis
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==2]<- 0
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==8]<- NA
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==9]<- NA

track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==1]<- 1 #Rheumatoid arthritis
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==2]<- 0
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==8]<- NA
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==9]<- NA

track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==1]<- 1 #Other arthritis
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==2]<- 0
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==8]<- NA
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==9]<- NA

track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==1]<- 1 #Diabetes
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==2]<- 0
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==8]<- NA
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==9]<- NA

track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==1]<- 1 #High blood pressure
track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==2]<- 0
track.BL$CCC_HBP_COM[track.BL$CCT_HBP_TRM==8]<- NA
track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==9]<- NA

track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==1]<- 1 #Under active thyroid
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==2]<- 0
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==8]<- NA
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==9]<- NA

track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==1]<- 1 #Angina
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==2]<- 0
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==8]<- NA
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==9]<- NA

track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==1]<- 1 #Stroke or CVA
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==2]<- 0
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==8]<- NA
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==9]<- NA

track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==1]<- 1 #myocardial infarction
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==2]<- 0
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==8]<- NA
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==9]<- NA

track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==1]<- 1 #Overactive thyroid
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==2]<- 0
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==8]<- NA
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==9]<- NA

track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==1]<- 1 #Transient Ischemic Attack
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==2]<- 0
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==8]<- NA
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==9]<- NA

track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==1]<- 1 #Asthma
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==2]<- 0
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==8]<- NA
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==9]<- NA

track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==1]<- 1 #Osteoperosis
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==2]<- 0
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==8]<- NA
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==9]<- NA

track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==1]<- 1 #Parkinsons
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==2]<- 0
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==8]<- NA
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==9]<- NA

track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==1]<- 1 #COPD
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==2]<- 0
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==8]<- NA
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==9]<- NA

track.BL$Chronic_conditions<-track.BL$CCT_HEART_TRM + track.BL$CCT_PVD_TRM + track.BL$CCT_MEMPB_TRM + track.BL$CCT_ALZH_TRM + track.BL$CCT_MS_TRM + 
  track.BL$CCT_EPIL_TRM + track.BL$CCT_MGRN_TRM + track.BL$CCT_ULCR_TRM +
  track.BL$CCT_IBDIBS_TRM + track.BL$CCT_BOWINC_TRM + track.BL$CCT_URIINC_TRM + track.BL$CCT_MACDEG_TRM + track.BL$CCT_CANC_TRM + track.BL$CCT_BCKP_TRM + track.BL$CCT_KIDN_TRM + 
  track.BL$CCT_OTCCT_TRM + track.BL$CCT_OAHAND_TRM + track.BL$CCT_OAHIP_TRM + track.BL$CCT_OAKNEE_TRM + track.BL$CCT_RA_TRM + track.BL$CCT_OTART_TRM +
  track.BL$CCT_DIAB_TRM + track.BL$CCT_HBP_TRM + track.BL$CCT_UTHYR_TRM + track.BL$CCT_ANGI_TRM + track.BL$CCT_CVA_TRM + track.BL$CCT_AMI_TRM + track.BL$CCT_OTHYR_TRM + 
  track.BL$CCT_TIA_TRM + track.BL$CCT_ASTHM_TRM + track.BL$CCT_OSTPO_TRM + track.BL$CCT_PARK_TRM + track.BL$CCT_COPD_TRM

#Restless Sleep (≥ 3-4 days/week)
track.BL$RSTLS_Sleep<-NA
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM<3]<-1
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM>=3 & track.BL$DEP_RSTLS_TRM<8]<-0
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM>4]<-NA

#Finalize data set

track.BL.1<-track.BL[c(1,3,4,72:80,101:109,82,111)]

names(track.BL.1) <-paste(names(track.BL.1),"_0", sep="")

track.BL.Final<- rename(track.BL.1, "ID" = "entity_id_0")

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Baseline (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
track.BLcogs<-trackBL[c(1,1010,1011,1068,1055,1067,1065,1066,995,1058)]

############Cognitive Function##############

#~~~~~~Animal Fluency~~~~~~~~~~~#
track.BLcogs$Animal_Fluency_Lang<-NA
track.BLcogs$Animal_Fluency_Lang[track.BLcogs$COG_AFT_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$Animal_Fluency_Lang[track.BLcogs$COG_AFT_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$Animal_Fluency_Strict<-track.BLcogs$COG_AFT_SCORE_1_TRM
track.BLcogs$Animal_Fluency_Lenient<-track.BLcogs$COG_AFT_SCORE_2_TRM



#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.BLcogs$MAT_Lang<-NA
track.BLcogs$MAT_Lang[track.BLcogs$COG_MAT_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$MAT_Lang[track.BLcogs$COG_MAT_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$MAT_Score<-track.BLcogs$COG_MAT_SCORE_TRM

#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.BLcogs$RVLT_Immediate_Lang<- NA
track.BLcogs$RVLT_Immediate_Lang[track.BLcogs$COG_REYI_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$RVLT_Immediate_Lang[track.BLcogs$COG_REYI_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$RVLT_Immediate_Score<-track.BLcogs$COG_REYI_SCORE_TRM

#Rey-Delayed Recall
track.BLcogs$RVLT_Delayed_Lang<- NA
track.BLcogs$RVLT_Delayed_Lang[track.BLcogs$COG_REYII_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$RVLT_Delayed_Lang[track.BLcogs$COG_REYII_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$RVLT_Delayed_Score<-track.BLcogs$COG_REYII_SCORE_TRM

track.BLcogs1 <- track.BLcogs[c(1,11:19)]

names(track.BLcogs1) <-paste(names(track.BLcogs1),"_0", sep="")

track.BLcogs.Final<- rename(track.BLcogs1, "ID" = "entity_id_0")

1.2.2) Follow-up 1 processing

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU1 (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP1")

trackFU1<-read.csv("/Users/ryan/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP1/23SP001_McMaster_PRaina_FUP1_Trav3-1.csv")#Comprehensive Cohort Baseline

track.FU1<-trackFU1[c(1,75,76,594,83,1105,78,1859,134,155,174,181,187,204,201,210,223,228,240,
                      244,252,140,157,163,176,183,190,256,258,1832,1842,444,448,495,496,770,
                      423,434,451,575,455,458,461,462,463,470,473,514,523,537,399,396,392,401,
                      404,419,413,519,426,437,430,522,441,407,512,412,546,223,731)]

#Sex (no variable in FU1)
SexFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id, Sex)
track.FU1$Sex<-SexFU1$Sex


#Age
track.FU1$Age<-track.FU1$AGE_NMBR_TRF1

#Marital Status
track.FU1$Relationship_status<-NA
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==1]<-"Single"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==2]<-"Married"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==3]<-"Widowed"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==4]<-"Divorced"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==5]<-"Separated"

#Education 4 Category (No variable in FU1)
EducationFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Education4)
track.FU1$Education4<-EducationFU1$Education4

#Household Income
track.FU1$Income_Level<-NA
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==1]<-"<$20k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==2]<-"$20-50k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==3]<-"$50-100k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==4]<-"$100-150k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==5]<-">$150k"

#Living Status
track.FU1$Living_status<-NA
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==1]<-"House"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==2 |track.FU1$OWN_DWLG_TRF1==6]<-"Apartment/Condo/Townhome"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==3]<-"Assisted Living"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==4 | track.FU1$OWN_DWLG_TRF1==5 | track.FU1$OWN_DWLG_TRF1>=7]<-"Other"

#Alcohol      
track.FU1$Alcohol<-NA
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==1]<-"Regular drinker (at least once a month)"
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==2]<-"Occasional drinker"
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==3]<-"Non-drinker"

#Smoking Status (no variable in FU1)
SmokingFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Smoking_Status)
track.FU1$Smoking_Status<-SmokingFU1$Smoking_Status

#Ethnicity (take from baseline)
EthnicityFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id, Ethnicity)
track.FU1$Ethnicity<-EthnicityFU1$Ethnicity

#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.FU1$PASE_Q1<-NA
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==1]<-0
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==2]<-0.11
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==3]<-0.25
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==4]<-0.43
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1>4]<-NA
track.FU1$PASE_Q1[is.na(track.FU1$PA2_SIT_TRF1)]<-NA

track.FU1$PASE_Q1B<-NA
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==1]<-0
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==2]<-1
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==3]<-3
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==4]<-6
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==5]<-10
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1>5]<- 0
track.FU1$PASE_Q1B[is.na(track.FU1$PA2_SITHR_TRF1)]<-NA
track.FU1$PASE_Q1B<-as.numeric(track.FU1$PASE_Q1B)

#Q2: Walking outside frequency
track.FU1$PASE_Q2<- NULL
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==1]<-0
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==2]<-0.11
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==3]<-0.25
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==4]<-0.43
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1>4]<-NA
track.FU1$PASE_Q2[is.na(track.FU1$PA2_WALK_TRF1)]<-NA

track.FU1$PASE_Q2A<- NA
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==1]<-0
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==2]<-1
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==3]<-3
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==4]<-6
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==5]<-10
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1>5]<-NA
track.FU1$PASE_Q2A[is.na(track.FU1$PA2_WALKHR_TRF1)]<-NA
track.FU1$PASE_Q2A<-as.numeric(track.FU1$PASE_Q2A)

#Q3: Light Sports or Activity Frequency
track.FU1$PASE_Q3<- NA
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==1]<-0
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1>4]<-NA
track.FU1$PASE_Q3[is.na(track.FU1$PA2_LSPRT_TRF1)]<-NA

track.FU1$PASE_Q3A<- NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1 == 1 | is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-0
track.FU1$PASE_Q3A[is.na(track.FU1$PA2_LSPRT_TRF1) & is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1>4 & is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q3A<-as.numeric(track.FU1$PASE_Q3A)

#Q4: Moderate Sports or Activity Frequency
track.FU1$PASE_Q4<-NA
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==1]<-0
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1>4]<-NA
track.FU1$PASE_Q4[is.na(track.FU1$PA2_MSPRT_TRF1)]<-NA

track.FU1$PASE_Q4A<- NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRT_TRF1 == 1 | is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-0
track.FU1$PASE_Q4A[is.na(track.FU1$PA2_MSPRT_TRF1) & is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRT_TRF1>4 & is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q4A<-as.numeric(track.FU1$PASE_Q4A)

#Q5: Strenuous Sports or Activity Frequency
track.FU1$PASE_Q5<-NA
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==1]<-0
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1>4]<-NA
track.FU1$PASE_Q5[is.na(track.FU1$PA2_SSPRT_TRF1)]<-NA

track.FU1$PASE_Q5A<- NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRT_TRF1 == 1 | is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-0
track.FU1$PASE_Q5A[is.na(track.FU1$PA2_SSPRT_TRF1) & is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRT_TRF1>4 & is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q5A<-as.numeric(track.FU1$PASE_Q5A)

#Q6: Muscle strengthening and endurance exercise
track.FU1$PASE_Q6<-NA
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==1]<-0
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==2]<-0.11
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==3]<-0.25
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==4]<-0.43
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1>4]<-NA
track.FU1$PASE_Q6[is.na(track.FU1$PA2_EXER_TRF1)]<-NA

track.FU1$PASE_Q6A<- NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==1]<-0
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==2]<-1
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==3]<-3
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==4]<-6
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==5]<-10
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1>5]<-NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXER_TRF1 == 1 | is.na(track.FU1$PA2_EXERHR_TRF1)]<-0
track.FU1$PASE_Q6A[is.na(track.FU1$PA2_EXER_TRF1) & is.na(track.FU1$PA2_EXERHR_TRF1)]<-NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXER_TRF1>4 & is.na(track.FU1$PA2_EXERHR_TRF1)]<-NA
track.FU1$PASE_Q6A<-as.numeric(track.FU1$PASE_Q6A)

#Q7: Light Housework
track.FU1$PASE_Q7<-NA
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1==1]<-1
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1==2]<-0
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1>2]<-NA
track.FU1$PASE_Q7[is.na(track.FU1$PA2_LTHSWK_TRF1)]<-NA
track.FU1$PASE_Q7<-as.numeric(track.FU1$PASE_Q7)

#Q8: Heavy Housework
track.FU1$PASE_Q8<-NA
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1==1]<-1
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1==2]<-0
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1>2]<-NA
track.FU1$PASE_Q8[is.na(track.FU1$PA2_HVYHSWK_TRF1)]<-NA
track.FU1$PASE_Q8<-as.numeric(track.FU1$PASE_Q8)

#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.FU1$PASE_Q9A<-NA
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1==1]<-1
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1==2]<-0
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1>2]<-NA
track.FU1$PASE_Q9A[is.na(track.FU1$PA2_HMREPAIR_TRF1)]<-NA
track.FU1$PASE_Q9A<-as.numeric(track.FU1$PASE_Q9A)

track.FU1$PASE_Q9B<-NA
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1==1]<-1
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1==2]<-0
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1>2]<-NA
track.FU1$PASE_Q9B[is.na(track.FU1$PA2_HVYODA_TRF1)]<-NA
track.FU1$PASE_Q9B<-as.numeric(track.FU1$PASE_Q9B)

track.FU1$PASE_Q9C<-NA
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1==1]<-1
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1==2]<-0
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1>2]<-NA
track.FU1$PASE_Q9C[is.na(track.FU1$PA2_LTODA_TRF1)]<-NA
track.FU1$PASE_Q9C<-as.numeric(track.FU1$PASE_Q9C)

track.FU1$PASE_Q9D<-NA
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1==1]<-1
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1==2]<-0
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1>2]<-NA
track.FU1$PASE_Q9D[is.na(track.FU1$PA2_CRPRSN_TRF1)]<-NA
track.FU1$PASE_Q9D<-as.numeric(track.FU1$PASE_Q9D)

#Q10: Working and Volunteering
track.FU1$PASE_Q10<-NA
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1==1]<-1
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1==2]<-0
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1>2]<-NA
track.FU1$PASE_Q10[is.na(track.FU1$PA2_WRK_TRF1)]<-NA
track.FU1$PASE_Q10<-as.numeric(track.FU1$PASE_Q10)

track.FU1$PASE_Q10A<-NA
track.FU1$PASE_Q10A<-track.FU1$PA2_WRKHRS_NB_TRF1
track.FU1$PASE_Q10A[track.FU1$PA2_WRKHRS_NB_TRF1>=700]<-NA
track.FU1$PASE_Q10A[track.FU1$PA2_WRK_TRF1 == 2 | is.na(track.FU1$PA2_WRKHRS_NB_TRF1)]<-0
track.FU1$PASE_Q10A[is.na(track.FU1$PA2_WRK_TRF1) & is.na(track.FU1$PA2_WRKHRS_NB_TRF1)]<-NA
track.FU1$PASE_Q10A<-as.numeric(track.FU1$PASE_Q10A)
track.FU1$PASE_Q10A<-track.FU1$PASE_Q10A/7

#PASE TOTAL SCORE#
track.FU1$PASE_TOTAL<-track.FU1$PASE_Q2*track.FU1$PASE_Q2A*20 + track.FU1$PASE_Q3*track.FU1$PASE_Q3A*21 +  track.FU1$PASE_Q4*track.FU1$PASE_Q4A*23 + track.FU1$PASE_Q5*track.FU1$PASE_Q5A*30 +
  track.FU1$PASE_Q6*track.FU1$PASE_Q6A*30 + (track.FU1$PASE_Q7+track.FU1$PASE_Q8)*25 + track.FU1$PASE_Q9A*30 + track.FU1$PASE_Q9B*36 + track.FU1$PASE_Q9C*20 + track.FU1$PASE_Q9D*35 + 
  track.FU1$PASE_Q10*track.FU1$PASE_Q10A*21

#BMI
track.FU1$BMI<-track.FU1$HWT_DBMI_TRF1
track.FU1$BMI[track.FU1$HWT_DBMI_TRF1>100]<-NA

#CESD-10
track.FU1$CESD_10<-track.FU1$DEP_CESD10_TRF1
track.FU1$CESD_10[track.FU1$DEP_CESD10_TRF1==98]<-NA
track.FU1$CESD_10[track.FU1$DEP_CESD10_TRF1<0]<-NA

#Subjective Cognitive Impairment
track.FU1$SCI<- NA
track.FU1$SCI[track.FU1$CCT_MEMPB_TRF1==1]<- "Yes"
track.FU1$SCI[track.FU1$CCT_MEMPB_TRF1==2]<- "No"

#Dementia and AD
track.FU1$Dementia<- NA
track.FU1$Dementia[track.FU1$CCT_ALZH_TRF1==1]<- "Yes"
track.FU1$Dementia[track.FU1$CCT_ALZH_TRF1==2]<- "No"

#Anxiety
track.FU1$Anxiety<- NA
track.FU1$Anxiety[track.FU1$CCT_ANXI_TRF1==1]<- "Yes"
track.FU1$Anxiety[track.FU1$CCT_ANXI_TRF1==2]<- "No"

#Mood Disorders
track.FU1$Mood_Disord<- NA
track.FU1$Mood_Disord[track.FU1$CCT_MOOD_TRF1==1]<- "Yes"
track.FU1$Mood_Disord[track.FU1$CCT_MOOD_TRF1==2]<- "No"

#Pet Ownership at Baseline
track.FU1$Pet_Owner<-NA
track.FU1$Pet_Owner[track.FU1$SSA_PET_TRF1==1]<-"Yes"
track.FU1$Pet_Owner[track.FU1$SSA_PET_TRF1==2]<-"No"

#Number of Chronic Conditions
track.FU1$Chronic_conditions<-NA

track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==1]<- 1 #Heart Disease
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==2]<- 0
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==8]<- NA
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==9]<- NA

track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==1]<- 1 #peripheral vascular disease
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==2]<- 0
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==8]<- NA
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==9]<- NA

track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==1]<- 1 #SCI
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==2]<- 0
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==8]<- NA
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==9]<- NA

track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==1]<- 1 #Alzheimers or demeinta
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==2]<- 0
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==8]<- NA
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==9]<- NA

track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==1]<- 1 #Multiple sclerosis
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==2]<- 0
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==8]<- NA
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==9]<- NA

track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==1]<- 1 #Epilepsy
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==2]<- 0
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==8]<- NA
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==9]<- NA

track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==1]<- 1 #Migraine headaches
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==2]<- 0
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==8]<- NA
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==9]<- NA

track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==1]<- 1 #Intenstinal or stomach ulcers
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==2]<- 0
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==8]<- NA
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==9]<- NA

track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==1]<- 1 #Bowel disorder
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==2]<- 0
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==8]<- NA
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==9]<- NA

track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==1]<- 1 #Bowel incontinence
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==2]<- 0
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==8]<- NA
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==9]<- NA

track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==1]<- 1 #Urinary incontinence
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==2]<- 0
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==8]<- NA
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==9]<- NA

track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==1]<- 1 #Macular degeneration
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==2]<- 0
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==8]<- NA
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==9]<- NA

track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==1]<- 1 #All-cause cancer
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==2]<- 0
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==8]<- NA
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==9]<- NA

track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==1]<- 1 #Back problems but not fibromyalgia or arthritis
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==2]<- 0
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==8]<- NA
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==9]<- NA

track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==1]<- 1 #Kidney disease
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==2]<- 0
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==8]<- NA
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==9]<- NA

track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==1]<- 1 #Other long term mental or physical condition
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==2]<- 0
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==8]<- NA
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==9]<- NA

track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==1]<- 1 #Hand arthritis
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==2]<- 0
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==8]<- NA
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==9]<- NA

track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==1]<- 1 #Hip arthritis
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==2]<- 0
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==8]<- NA
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==9]<- NA

track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==1]<- 1 #Knee arthritis
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==2]<- 0
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==8]<- NA
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==9]<- NA

track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==1]<- 1 #Rheumatoid arthritis
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==2]<- 0
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==8]<- NA
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==9]<- NA

track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==1]<- 1 #Other arthritis
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==2]<- 0
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==8]<- NA
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==9]<- NA

track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==1]<- 1 #Diabetes
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==2]<- 0
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==8]<- NA
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==9]<- NA

track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==1]<- 1 #High blood pressure
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==2]<- 0
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==8]<- NA
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==9]<- NA

track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==1]<- 1 #Under active thyroid
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==2]<- 0
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==8]<- NA
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==9]<- NA

track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==1]<- 1 #Angina
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==2]<- 0
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==8]<- NA
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==9]<- NA

track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==1]<- 1 #Stroke or CVA
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==2]<- 0
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==8]<- NA
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==9]<- NA

track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==1]<- 1 #myocardial infarction
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==2]<- 0
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==8]<- NA
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==9]<- NA

track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==1]<- 1 #Overactive thyroid
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==2]<- 0
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==8]<- NA
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==9]<- NA

track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==1]<- 1 #Transient Ischemic Attack
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==2]<- 0
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==8]<- NA
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==9]<- NA

track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==1]<- 1 #Asthma
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==2]<- 0
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==8]<- NA
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==9]<- NA

track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==1]<- 1 #Osteoperosis
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==2]<- 0
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==8]<- NA
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==9]<- NA

track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==1]<- 1 #Parkinsons
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==2]<- 0
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==8]<- NA
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==9]<- NA

track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==1]<- 1 #COPD
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==2]<- 0
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==8]<- NA
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==9]<- NA

track.FU1$Chronic_conditions<-track.FU1$CCT_HEART_TRM + track.FU1$CCT_PVD_TRM + track.FU1$CCT_MEMPB_TRM + track.FU1$CCT_ALZH_TRM + track.FU1$CCT_MS_TRM +
  track.FU1$CCT_EPIL_TRM + track.FU1$CCT_MGRN_TRM + track.FU1$CCT_ULCR_TRM +
  track.FU1$CCT_IBDIBS_TRM + track.FU1$CCT_BOWINC_TRM + track.FU1$CCT_URIINC_TRM + track.FU1$CCT_MACDEG_TRM + track.FU1$CCT_CANC_TRM + track.FU1$CCT_BCKP_TRM + track.FU1$CCT_KIDN_TRM + 
  track.FU1$CCT_OTCCT_TRM + track.FU1$CCT_OAHAND_TRM + track.FU1$CCT_OAHIP_TRM + track.FU1$CCT_OAKNEE_TRM + track.FU1$CCT_RA_TRM + track.FU1$CCT_OTART_TRM +
  track.FU1$CCT_DIAB_TRM + track.FU1$CCT_HBP_TRM + track.FU1$CCT_UTHYR_TRM + track.FU1$CCT_ANGI_TRM + track.FU1$CCT_CVA_TRM + track.FU1$CCT_AMI_TRM + track.FU1$CCT_OTHYR_TRM + 
  track.FU1$CCT_TIA_TRM + track.FU1$CCT_ASTHM_TRM + track.FU1$CCT_OSTPO_TRM + track.FU1$CCT_PARK_TRM + track.FU1$CCT_COPD_TRM

#Restless Sleep (≥ 3-4 days/week)
track.FU1$RSTLS_Sleep<-NA
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1<3]<-1
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1>=3 & track.FU1$DEP_RSTLS_TRF1<8]<-0
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1>4]<-NA

#Finalize data set

track.FU1.1<-track.FU1[c(1:3,70:78,99:107,80,141)]

names(track.FU1.1) <-paste(names(track.FU1.1),"_1", sep="")

track.FU1.Final<- rename(track.FU1.1, "ID" = "entity_id_1")

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU1 (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
track.FU1cogs<-trackFU1[c(1,1927,1928,1876,1875,1966,1877,1914,1878,1969)]

############Cognitive Function##############

#~~~~~~Animal Fluency~~~~~~~~~~~#
track.FU1cogs$Animal_Fluency_Lang<-NA
track.FU1cogs$Animal_Fluency_Lang[track.FU1cogs$COG_AFT_STARTLANG_TRF1=="en"]<-"English"
track.FU1cogs$Animal_Fluency_Lang[track.FU1cogs$COG_AFT_STARTLANG_TRF1=="fr"]<-"French"

track.FU1cogs$Animal_Fluency_Strict<-track.FU1cogs$COG_AFT_SCORE_1_TRF1
track.FU1cogs$Animal_Fluency_Strict[track.FU1cogs$Animal_Fluency_Strict<0]<-NA
track.FU1cogs$Animal_Fluency_Lenient<-track.FU1cogs$COG_AFT_SCORE_2_TRF1
track.FU1cogs$Animal_Fluency_Lenient[track.FU1cogs$Animal_Fluency_Lenient<0]<-NA



#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.FU1cogs$MAT_Lang<-NA
track.FU1cogs$MAT_Lang[track.FU1cogs$COG_MAT_STARTLANG_TRF1=="en"]<-"English"
track.FU1cogs$MAT_Lang[track.FU1cogs$COG_MAT_STARTLANG_TRF1=="fr"]<-"French"

track.FU1cogs$MAT_Score<-track.FU1cogs$COG_MAT_SCORE_TRF1
track.FU1cogs$MAT_Score[track.FU1cogs$MAT_Score<0]<-NA

#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.FU1cogs$RVLT_Immediate_Lang<- NA
track.FU1cogs$RVLT_Immediate_Lang[track.FU1cogs$COG_REYI_LANG_TRF1=="en"]<-"English"
track.FU1cogs$RVLT_Immediate_Lang[track.FU1cogs$COG_REYI_LANG_TRF1=="fr"]<-"French"

track.FU1cogs$RVLT_Immediate_Score<-track.FU1cogs$COG_REYI_SCORE_TRF1
track.FU1cogs$RVLT_Immediate_Score[track.FU1cogs$RVLT_Immediate_Score<0]<-NA


#Rey-Delayed Recall
track.FU1cogs$RVLT_Delayed_Lang<- NA
track.FU1cogs$RVLT_Delayed_Lang[track.FU1cogs$COG_REYII_LANG_TRF1=="en"]<-"English"
track.FU1cogs$RVLT_Delayed_Lang[track.FU1cogs$COG_REYII_LANG_TRF1=="fr"]<-"French"

track.FU1cogs$RVLT_Delayed_Score<-track.FU1cogs$COG_REYII_SCORE_TRF1
track.FU1cogs$RVLT_Delayed_Score[track.FU1cogs$RVLT_Delayed_Score<0]<-NA

track.FU1cogs1 <- track.FU1cogs[c(1,11:19)]

names(track.FU1cogs1) <-paste(names(track.FU1cogs1),"_1", sep="")

track.FU1cogs.Final<- rename(track.FU1cogs1, "ID" = "entity_id_1")

1.2.3) Follow-up 2 processing

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU2 (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP2")

trackFU2<-read.csv("23SP001_McMaster_PRaina_FUP2_Trav1.csv")#Comprehensive Cohort Baseline

track.FU2<-trackFU2[c(1:5,47,1239,14,78,98,111,112,113,114,194,137,157,158,184,185,
                      191,195:202,50:54,685:694,415,419,514,517,732,389,401,423,604,427,
                      431,437,441,445,457,461,542,359,356,352,363,381,375,534,393,403,397,
                      538,407,367,530,371,568)]

#Sex (no variable in FU1)
SexFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id, Sex)
track.FU2$Sex<-SexFU2$Sex

#Age
track.FU2$Age<-track.FU2$AGE_NMBR_TRF2

#Marital Status
track.FU2$Relationship_status<-NA
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==1]<-"Single"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==2]<-"Married"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==3]<-"Widowed"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==4]<-"Divorced"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==5]<-"Separated"

#Education 4 Category (No variable in FU2)
EducationFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Education4)
track.FU2$Education4<-EducationFU2$Education4

#Household Income
track.FU2$Income_Level<-NA
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==1]<-"<$20k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==2]<-"$20-50k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==3]<-"$50-100k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==4]<-"$100-150k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==5]<-">$150k"

#Living Status
track.FU2$Living_status<-NA
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==1]<-"House"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==2 |track.FU2$OWN_DWLG_TRF2==6]<-"Apartment/Condo/Townhome"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==3]<-"Assisted Living"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==4 | track.FU2$OWN_DWLG_TRF2==5 | track.FU2$OWN_DWLG_TRF2>=7]<-"Other"

#Alcohol (Based on a different question/scale)     
track.FU2$Alcohol<-NA
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2<=6]<-"Regular drinker (at least once a month)"
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2==7]<-"Occasional drinker"
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2==96]<-"Non-drinker"

#Smoking Status (no variable in FU2)
SmokingFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Smoking_Status)
track.FU2$Smoking_Status<-SmokingFU2$Smoking_Status

#Ethnicity (take from baseline))
EthnicityFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Ethnicity)
track.FU2$Ethnicity<-EthnicityFU2$Ethnicity

#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.FU2$PASE_Q1<-NA
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==1]<-0
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==2]<-0.11
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==3]<-0.25
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==4]<-0.43
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2>4]<-NA
track.FU2$PASE_Q1[is.na(track.FU2$PA2_SIT_TRF2)]<-NA

track.FU2$PASE_Q1B<-NA
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==1]<-0
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==2]<-1
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==3]<-3
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==4]<-6
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==5]<-10
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2>5]<- 0
track.FU2$PASE_Q1B[is.na(track.FU2$PA2_SITHR_SIT_TRF2)]<-NA
track.FU2$PASE_Q1B<-as.numeric(track.FU2$PASE_Q1B)

#Q2: Walking outside frequency
track.FU2$PASE_Q2<- NULL
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==1]<-0
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==2]<-0.11
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==3]<-0.25
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==4]<-0.43
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2>4]<-NA
track.FU2$PASE_Q2[is.na(track.FU2$PA2_WALK_TRF2)]<-NA

track.FU2$PASE_Q2A<- NA
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==1]<-0
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==2]<-1
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==3]<-3
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==4]<-6
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==5]<-10
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2>5]<-NA
track.FU2$PASE_Q2A[is.na(track.FU2$PA2_WALKHR_TRF2)]<-NA
track.FU2$PASE_Q2A<-as.numeric(track.FU2$PASE_Q2A)

#Q3: Light Sports or Activity Frequency
track.FU2$PASE_Q3<- NA
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==1]<-0
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2>4]<-NA
track.FU2$PASE_Q3[is.na(track.FU2$PA2_LSPRT_TRF2)]<-NA

track.FU2$PASE_Q3A<- NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2 == 1 | is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-0
track.FU2$PASE_Q3A[is.na(track.FU2$PA2_LSPRTHR_TRF2) & is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2>4 & is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-NA
track.FU2$PASE_3A<-as.numeric(track.FU2$PASE_Q3A)

#Q4: Moderate Sports or Activity Frequency
track.FU2$PASE_Q4<-NA
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==1]<-0
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2>4]<-NA
track.FU2$PASE_Q4[is.na(track.FU2$PA2_MSPRT_TRF2)]<-NA

track.FU2$PASE_Q4A<- NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRT_TRF2 == 1 | is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-0
track.FU2$PASE_Q4A[is.na(track.FU2$PA2_MSPRT_TRF2) & is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRT_TRF2>4 & is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q4A<-as.numeric(track.FU2$PASE_Q4A)

#Q5: Strenuous Sports or Activity Frequency
track.FU2$PASE_Q5<-NA
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==1]<-0
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2>4]<-NA
track.FU2$PASE_Q5[is.na(track.FU2$PA2_SSPRT_TRF2)]<-NA

track.FU2$PASE_Q5A<- NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRT_TRF2 == 1 | is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-0
track.FU2$PASE_Q5A[is.na(track.FU2$PA2_SSPRT_TRF2) & is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRT_TRF2>4 & is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q5A<-as.numeric(track.FU2$PASE_Q5A)

#Q6: Muscle strengthening and endurance exercise
track.FU2$PASE_Q6<-NA
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==1]<-0
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==2]<-0.11
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==3]<-0.25
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==4]<-0.43
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2>4]<-NA
track.FU2$PASE_Q6[is.na(track.FU2$PA2_EXER_TRF2)]<-NA

track.FU2$PASE_Q6A<- NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==1]<-0
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==2]<-1
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==3]<-3
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==4]<-6
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==5]<-10
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2>5]<-NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXER_TRF2 == 1 | is.na(track.FU2$PA2_EXERHR_TRF2)]<-0
track.FU2$PASE_Q6A[is.na(track.FU2$PA2_EXER_TRF2) & is.na(track.FU2$PA2_EXERHR_TRF2)]<-NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXER_TRF2>4 & is.na(track.FU2$PA2_EXERHR_TRF2)]<-NA
track.FU2$PASE_Q6A<-as.numeric(track.FU2$PASE_Q6A)

#Q7: Light Housework
track.FU2$PASE_Q7<-NA
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2==1]<-1
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2==2]<-0
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2>2]<-NA
track.FU2$PASE_Q7[is.na(track.FU2$PA2_LTHSWK_TRF2)]<-NA
track.FU2$PASE_Q7<-as.numeric(track.FU2$PASE_Q7)

#Q8: Heavy Housework
track.FU2$PASE_Q8<-NA
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2==1]<-1
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2==2]<-0
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2>2]<-NA
track.FU2$PASE_Q8[is.na(track.FU2$PA2_HVYHSWK_TRF2)]<-NA
track.FU2$PASE_Q8<-as.numeric(track.FU2$PASE_Q8)

#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.FU2$PASE_Q9A<-NA
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2==1]<-1
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2==2]<-0
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2>2]<-NA
track.FU2$PASE_Q9A[is.na(track.FU2$PA2_HMREPAIR_TRF2)]<-NA
track.FU2$PASE_Q9A<-as.numeric(track.FU2$PASE_Q9A)

track.FU2$PASE_Q9B<-NA
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2==1]<-1
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2==2]<-0
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2>2]<-NA
track.FU2$PASE_Q9B[is.na(track.FU2$PA2_HVYODA_TRF2)]<-NA
track.FU2$PASE_Q9B<-as.numeric(track.FU2$PASE_Q9B)

track.FU2$PASE_Q9C<-NA
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2==1]<-1
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2==2]<-0
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2>2]<-NA
track.FU2$PASE_Q9C[is.na(track.FU2$PA2_LTODA_TRF2)]<-NA
track.FU2$PASE_Q9C<-as.numeric(track.FU2$PASE_Q9C)

track.FU2$PASE_Q9D<-NA
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2==1]<-1
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2==2]<-0
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2>2]<-NA
track.FU2$PASE_Q9D[is.na(track.FU2$PA2_CRPRSN_TRF2)]<-NA
track.FU2$PASE_Q9D<-as.numeric(track.FU2$PASE_Q9D)

#Q10: Working and Volunteering
track.FU2$PASE_Q10<-NA
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2==1]<-1
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2==2]<-0
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2>2]<-NA
track.FU2$PASE_Q10[is.na(track.FU2$PA2_WRK_TRF2)]<-NA
track.FU2$PASE_Q10<-as.numeric(track.FU2$PASE_Q10)

track.FU2$PASE_Q10A<-NA
track.FU2$PASE_Q10A<-track.FU2$PA2_WRKHRS_NB_TRF2
track.FU2$PASE_Q10A[track.FU2$PA2_WRKHRS_NB_TRF2>=700]<-NA
track.FU2$PASE_Q10A[track.FU2$PA2_WRK_TRF2 == 2 | is.na(track.FU2$PA2_WRKHRS_NB_TRF2)]<-0
track.FU2$PASE_Q10A[is.na(track.FU2$PA2_WRK_TRF2) & is.na(track.FU2$PA2_WRKHRS_NB_TRF2)]<-NA
track.FU2$PASE_Q10A<-as.numeric(track.FU2$PASE_Q10A)
track.FU2$PASE_Q10A<-track.FU2$PASE_Q10A/7

#PASE TOTAL SCORE#
track.FU2$PASE_TOTAL<-track.FU2$PASE_Q2*track.FU2$PASE_Q2A*20 + track.FU2$PASE_Q3*track.FU2$PASE_Q3A*21 +  track.FU2$PASE_Q4*track.FU2$PASE_Q4A*23 + track.FU2$PASE_Q5*track.FU2$PASE_Q5A*30 +
  track.FU2$PASE_Q6*track.FU2$PASE_Q6A*30 + (track.FU2$PASE_Q7+track.FU2$PASE_Q8)*25 + track.FU2$PASE_Q9A*30 + track.FU2$PASE_Q9B*36 + track.FU2$PASE_Q9C*20 + track.FU2$PASE_Q9D*35 + 
  track.FU2$PASE_Q10*track.FU2$PASE_Q10A*21


#BMI (Not included)
track.FU2$Height<-NA
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==1]<-36
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==2]<-37
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==3]<-38
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==4]<-39
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==5]<-40
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==6]<-41
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==7]<-42
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==8]<-43
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==9]<-44
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==10]<-45
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==11]<-46
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==12]<-47
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==1]<-48
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==2]<-49
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==3]<-50
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==4]<-51
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==5]<-52
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==6]<-53
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==7]<-54
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==8]<-55
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==9]<-56
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==10]<-57
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==11]<-58
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==12]<-59
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==1]<-60
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==2]<-61
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==3]<-62
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==4]<-63
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==5]<-64
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==6]<-65
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==7]<-66
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==8]<-67
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==9]<-68
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==10]<-69
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==11]<-70
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==12]<-71
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==1]<-72
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==2]<-73
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==3]<-74
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==4]<-75
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==5]<-76
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==6]<-77
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==7]<-78
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==8]<-79
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==9]<-80
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==10]<-81
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==11]<-82
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==12]<-83
track.FU2$Height<-as.numeric(track.FU2$Height)*0.0254

track.FU2$Weight<-track.FU2$HWT_WGHT_NB_TRF2
track.FU2$Weight[track.FU2$HWT_WGHT_NB_TRF2>500]<-NA
track.FU2$Weight[track.FU2$HWT_WGHT_NB_TRF2==-8888]<-NA

track.FU2$BMI<-track.FU2$Weight/((track.FU2$Height)^2)

#CESD-10 (NO CESD variable in the data set; need to calculate by hand)
#Q1: I was bothered by things that usually don’t bother me.
track.FU2$CESD_Q1<-NA
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==1]<-3
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==2]<-2
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==3]<-1
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==4]<-0
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2>4]<-NA
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2<0]<-NA

#Q2: I had trouble keeping my mind on what I was doing.
track.FU2$CESD_Q2<-NA
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==1]<-3
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==2]<-2
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==3]<-1
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==4]<-0
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2>4]<-NA
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2<0]<-NA

#Q3: I felt depressed
track.FU2$CESD_Q3<-NA
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==1]<-3
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==2]<-2
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==3]<-1
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==4]<-0
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2>4]<-NA
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2<0]<-NA

#Q4: I felt that everything I did was an effort.
track.FU2$CESD_Q4<-NA
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==1]<-3
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==2]<-2
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==3]<-1
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==4]<-0
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2>4]<-NA
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2<0]<-NA

#Q5: I felt hopeful about the future
track.FU2$CESD_Q5<-NA
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==1]<-0
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==2]<-1
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==3]<-2
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==4]<-3
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2>4]<-NA
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2<0]<-NA

#Q6: I felt fearful.
track.FU2$CESD_Q6<-NA
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==1]<-3
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==2]<-2
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==3]<-1
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==4]<-0
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2>4]<-NA
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2<0]<-NA

#Q7: My sleep was restless
track.FU2$CESD_Q7<-NA
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==1]<-3
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==2]<-2
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==3]<-1
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==4]<-0
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2>4]<-NA
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2<0]<-NA

#Q8: I was happy
track.FU2$CESD_Q8<-NA
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==1]<-0
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==2]<-1
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==3]<-2
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==4]<-3
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2>4]<-NA
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2<0]<-NA

#Q9: I felt lonely
track.FU2$CESD_Q9<-NA
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==1]<-3
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==2]<-2
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==3]<-1
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==4]<-0
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2>4]<-NA
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2<0]<-NA

#Q10: I could not “get going.”
track.FU2$CESD_Q10<-NA
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==1]<-3
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==2]<-2
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==3]<-1
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==4]<-0
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2>4]<-NA
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2<0]<-NA

track.FU2$CESD_10<-track.FU2$CESD_Q1 + track.FU2$CESD_Q2 + track.FU2$CESD_Q3 + track.FU2$CESD_Q4 + track.FU2$CESD_Q5 + 
  track.FU2$CESD_Q6 + track.FU2$CESD_Q7 + track.FU2$CESD_Q8 + track.FU2$CESD_Q9 + track.FU2$CESD_Q10

#Subjective Cognitive Impairment
track.FU2$SCI<- NA
track.FU2$SCI[track.FU2$CCT_MEMPB_TRF2==1]<- "Yes"
track.FU2$SCI[track.FU2$CCT_MEMPB_TRF2==2]<- "No"

#Dementia and AD
track.FU2$Dementia<- NA
track.FU2$Dementia[track.FU2$CCT_ALZH_TRF2==1]<- "Yes"
track.FU2$Dementia[track.FU2$CCT_ALZH_TRF2==2]<- "No"

#Anxiety
track.FU2$Anxiety<- NA
track.FU2$Anxiety[track.FU2$CCT_ANXI_TRF2==1]<- "Yes"
track.FU2$Anxiety[track.FU2$CCT_ANXI_TRF2==2]<- "No"

#Mood Disorders
track.FU2$Mood_Disord<- NA
track.FU2$Mood_Disord[track.FU2$CCT_MOOD_TRF2==1]<- "Yes"
track.FU2$Mood_Disord[track.FU2$CCT_MOOD_TRF2==2]<- "No"

#Number of Chronic Conditions
track.FU2$Chronic_conditions<-NA

#Pet Ownership at Baseline
track.FU2$Pet_Owner<-NA
track.FU2$Pet_Owner[track.FU2$SSA_PET_TRF2==1]<-"Yes"
track.FU2$Pet_Owner[track.FU2$SSA_PET_TRF2==2]<-"No"


track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==1]<- 1 #Heart Disease
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==2]<- 0
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==8]<- NA
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==9]<- NA

track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==1]<- 1 #peripheral vascular disease (called Peripheral Artery Disease, unlike Baseline or FU1)
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==2]<- 0
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==8]<- NA
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==9]<- NA

track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==1]<- 1 #SCI
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==2]<- 0
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==8]<- NA
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==9]<- NA

track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==1]<- 1 #Alzheimers or demeinta
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==2]<- 0
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==8]<- NA
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==9]<- NA

track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==1]<- 1 #Multiple sclerosis
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==2]<- 0
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==8]<- NA
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==9]<- NA

track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==1]<- 1 #Epilepsy (Different question)
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==2]<- 0
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==3]<- NA
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==8]<- NA
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==9]<- NA

track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==1]<- 1 #Migraine headaches
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==2]<- 0
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==8]<- NA
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==9]<- NA

track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==1]<- 1 #Intenstinal or stomach ulcers
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==2]<- 0
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==8]<- NA
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==9]<- NA

track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==1]<- 1 #Bowel disorder (different name for IBS and IBD)
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==2]<- 0
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==8]<- NA
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==9]<- NA

track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==1]<- 1 #Bowel incontinence
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==2]<- 0
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==8]<- NA
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==9]<- NA

track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==1]<- 1 #Urinary incontinence
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==2]<- 0
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==8]<- NA
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==9]<- NA

track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==1]<- 1 #Macular degeneration
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==2]<- 0
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==8]<- NA
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==9]<- NA

track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==1]<- 1 #All-cause cancer
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==2]<- 0
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==8]<- NA
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==9]<- NA

track.FU2$CCT_BCKP_TRM<-NA #Back problems but not fibromyalgia or arthritis (Question not included)

track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==1]<- 1 #Kidney disease
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==2]<- 0
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==8]<- NA
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==9]<- NA

track.FU2$CCT_OTCCT_TRM <- NA #Other long term mental or physical condition (Question not included)

track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==1]<- 1 #Hand arthritis
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==2]<- 0
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==8]<- NA
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==9]<- NA

track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==1]<- 1 #Hip arthritis
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==2]<- 0
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==8]<- NA
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==9]<- NA

track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==1]<- 1 #Knee arthritis
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==2]<- 0
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==8]<- NA
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==9]<- NA

track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==1]<- 1 #Rheumatoid arthritis
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==2]<- 0
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==8]<- NA
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==9]<- NA

track.FU2$CCT_ARTOT_TRM <- NA #Other arthritis (not included in the data)

track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==1]<- 1 #Diabetes
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==2]<- 0
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==8]<- NA
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==9]<- NA

track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==1]<- 1 #High blood pressure
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==2]<- 0
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==8]<- NA
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==9]<- NA

track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==1]<- 1 #Under active thyroid
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==2]<- 0
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==8]<- NA
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==9]<- NA

track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==1]<- 1 #Angina
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==2]<- 0
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==8]<- NA
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==9]<- NA

track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==1]<- 1 #Stroke or CVA
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==2]<- 0
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==8]<- NA
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==9]<- NA

track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==1]<- 1 #myocardial infarction
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==2]<- 0
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==8]<- NA
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==9]<- NA

track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==1]<- 1 #Overactive thyroid
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==2]<- 0
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==8]<- NA
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==9]<- NA

track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==1]<- 1 #Transient Ischemic Attack
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==2]<- 0
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==8]<- NA
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==9]<- NA

track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==1]<- 1 #Asthma
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==2]<- 0
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==8]<- NA
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==9]<- NA

track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==1]<- 1 #Osteoperosis
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==2]<- 0
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==8]<- NA
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==9]<- NA

track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==1]<- 1 #Parkinsons
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==2]<- 0
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==8]<- NA
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==9]<- NA

track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==1]<- 1 #COPD
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==2]<- 0
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==8]<- NA
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==9]<- NA

track.FU2$Chronic_conditions<-track.FU2$CCT_HEART_TRM + track.FU2$CCT_PVD_TRM + track.FU2$CCT_MEMPB_TRM + track.FU2$CCT_ALZH_TRM + track.FU2$CCT_MS_TRM + 
  track.FU2$CCT_EPIL_TRM + track.FU2$CCT_MGRN_TRM + track.FU2$CCT_ULCR_TRM +
  track.FU2$CCT_IBDIBS_TRM + track.FU2$CCT_BOWINC_TRM + track.FU2$CCT_URIINC_TRM + track.FU2$CCT_MACDEG_TRM + track.FU2$CCT_CANC_TRM + track.FU2$CCT_KIDN_TRM + 
  track.FU2$CCT_OAHAND_TRM + track.FU2$CCT_OAHIP_TRM + track.FU2$CCT_OAKNEE_TRM + track.FU2$CCT_RA_TRM  +
  track.FU2$DIA_DIAB_TRM + track.FU2$CCT_HBP_TRM + track.FU2$CCT_UTHYR_TRM + track.FU2$CCT_ANGI_TRM + track.FU2$CCT_CVA_TRM + track.FU2$CCT_AMI_TRM + track.FU2$CCT_OTHYR_TRM + 
  track.FU2$CCT_TIA_TRM + track.FU2$CCT_ASTHM_TRM + track.FU2$CCT_OSTPO_TRM + track.FU2$CCT_PARK_TRM + track.FU2$CCT_COPD_TRM

#Restless Sleep (≥ 3-4 days/week)
track.FU2$RSTLS_Sleep<-NA
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2<3]<-1
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2>=3 & track.FU2$DEP_RSTLS_TRF2<8]<-0
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2>4]<-NA

#Finalize data set

track.FU2.1<-track.FU2[c(1:3,78:86,108,111,122:128,88,162)]

names(track.FU2.1) <-paste(names(track.FU2.1),"_2", sep="")

track.FU2.Final<- rename(track.FU2.1, "ID" = "entity_id_2")

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU2 (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP2_COG")

trackFU2cogs<-read.csv("23SP001_McMaster_PRaina_Cognition_Tra_FUP2_v0.csv")#Comprehensive Cohort Baseline

track.FU2cogs<-trackFU2cogs[c(1,64:66,110,109,41,42,48,47)]

############Cognitive Function##############

#~~~~~~Animal Fluency~~~~~~~~~~~#
track.FU2cogs$Animal_Fluency_Strict<-track.FU2cogs$COG_AFT_SCORE_1_TRF2
track.FU2cogs$Animal_Fluency_Lenient<-track.FU2cogs$COG_AFT_SCORE_2_TRF2
track.FU2cogs$Animal_Fluency_Lenient[track.FU2cogs$Animal_Fluency_Lenient<0]<-NA
track.FU2cogs$Animal_Fluency_Strict[track.FU2cogs$Animal_Fluency_Strict<0]<-NA

track.FU2cogs$Animal_Fluency_Lang<-NA
track.FU2cogs$Animal_Fluency_Lang[track.FU2cogs$COG_AFT_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$Animal_Fluency_Lang[track.FU2cogs$COG_AFT_STARTLANG_TRF2=="fr"]<-"French"

#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.FU2cogs$MAT_Lang<-NA
track.FU2cogs$MAT_Lang[track.FU2cogs$COG_MAT_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$MAT_Lang[track.FU2cogs$COG_MAT_STARTLANG_TRF2=="fr"]<-"French"

track.FU2cogs$MAT_Score<-track.FU2cogs$COG_MAT_SCORE_TRF2

track.FU2cogs$MAT_Score[track.FU2cogs$MAT_Score<0]<-NA


#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.FU2cogs$RVLT_Immediate_Lang<- NA
track.FU2cogs$RVLT_Immediate_Lang[track.FU2cogs$COG_REYI_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$RVLT_Immediate_Lang[track.FU2cogs$COG_REYI_STARTLANG_TRF2=="fr"]<-"French"

track.FU2cogs$RVLT_Immediate_Score<-track.FU2cogs$COG_REYI_SCORE_TRF2

track.FU2cogs$RVLT_Immediate_Score[track.FU2cogs$RVLT_Immediate_Score<0]<-NA

#Rey-Delayed Recall
track.FU2cogs$RVLT_Delayed_Lang<- NA
track.FU2cogs$RVLT_Delayed_Lang[track.FU2cogs$COG_REYII_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$RVLT_Delayed_Lang[track.FU2cogs$COG_REYII_STARTLANG_TRF2=="fr"]<-"French"

track.FU2cogs$RVLT_Delayed_Score<-track.FU2cogs$COG_REYII_SCORE_TRF2

track.FU2cogs$RVLT_Delayed_Score[track.FU2cogs$RVLT_Delayed_Score<0]<-NA

track.FU2cogs1 <- track.FU2cogs[c(1,11:19)]

names(track.FU2cogs1) <-paste(names(track.FU2cogs1),"_2", sep="")

track.FU2cogs.Final<- rename(track.FU2cogs1, "ID" = "entity_id_2")

1.2.3) COVID Data Processing (only include IDs and COVID information)

setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_Covid")

COVID<-read.csv("23SP001_McMaster_PRaina_COVID-19 Combined_v1-1.csv")#COVID Data

COVID_2 <- COVID[c(1,79)]

COVID_2<- rename(COVID_2, "ID" = "entity_id")

1.3) Data merging

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Combine Datasets
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#Combine Baseline dataframes
Track.Baseline.Final <- merge(track.BL.Final, track.BLcogs.Final, by = "ID")

#Combine FU1 dataframes
Track.FU1.Final <- merge(track.FU1.Final, track.FU1cogs.Final, by = "ID")

#Combine FU2 dataframes
Track.FU2.Final <- merge(track.FU2.Final, track.FU2cogs.Final, by = "ID")

#Combine data across different time points into full data set
Track.BL.FU1 <- merge(Track.Baseline.Final, Track.FU1.Final, by = "ID")

Track.Full <- merge(Track.BL.FU1, Track.FU2.Final, by = "ID")

#Combine full data frame with COVID data
Track.Full.COVID <- merge(Track.Full, COVID_2, by = "ID")

2) Final data-set development

This section details how our final data-set was developed. Our final data set included cognitively healthy participants at baseline, FU1, and FU2 with complete baseline neuropsycholoigcal testing (including education level, which was necessary for standardized scores). Thus our final sample size included in our linear mixed models was N=11,355.

Track.Final_Data <- subset(Track.Full, SCI_0=="No" & Dementia_0=="No" & SCI_1=="No" & Dementia_1 =="No" & 
                       SCI_2=="No" & Dementia_2 == "No" & !is.na(Animal_Fluency_Strict_0) & !is.na(MAT_Score_0) & !is.na(Education4_0) &
                       !is.na(RVLT_Immediate_Score_0) & !is.na(RVLT_Delayed_Score_0))

We then normalized all cognitive variables for language, age, and biological sex. Score standardization is dependent upon the following steps:

  1. Create a predicted score based on age, sex, and test language
  2. Create residualized score (i.e., obtained - predicted)
  3. Develop a standardized residual score adjusted for age, sex, and education
  4. Develop standardized z-score adjusted for age, sex, and education
  5. Develop standardized score using z-score with a mean of 10 and a SD of 3.
Tracking.Adjusted_Full <- Track.Final_Data %>%
  mutate(
    #Baseline Scores
    
    #RVLT Immediate Score
    RVLT_Immediate_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.768 - 0.050*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.449 - 0.036*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 10.095 - 0.073*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.686 - 0.064*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 8.077 - 0.043*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 9.806 - 0.059*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 9.161 - 0.047*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.804 - 0.053*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 5.666 - 0.025*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.953 - 0.067*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 7.662 - 0.039*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 8.829 - 0.057*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 6.976 - 0.031*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.667 - 0.045*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 9.502 - 0.061*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 9.013 - 0.047*Age_0
    ),
    RVLT_Immediate_Residual_0 = RVLT_Immediate_Score_0 - RVLT_Immediate_Predicted_0,
    RVLT_Immediate_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.471,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.525,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.611,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.675,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.528,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.643,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.694,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.802,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.290,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.473,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.913,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.641,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.623,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.595,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.605,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.715
    ),
    RVLT_Immediate_Normed_0 = RVLT_Immediate_Z_0*3 +10,
    RVLT_Immediate_Normed_0 = if_else(RVLT_Immediate_Normed_0 < 0, 0.01, RVLT_Immediate_Normed_0),
    
    #RVLT Delayed Score
    RVLT_Delayed_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.628 - 0.062*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.851 - 0.058*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.289 - 0.076*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.165 - 0.070*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 7.163 - 0.055*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 8.115 - 0.063*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.151 - 0.059*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.844 - 0.066*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 4.802 - 0.036*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 8.219 - 0.083*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.721 - 0.097*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 7.048 - 0.055*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.280 - 0.044*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.999 - 0.042*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.081 - 0.074*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 8.712 - 0.066*Age_0
    ),
    RVLT_Delayed_Residual_0 = RVLT_Delayed_Score_0 - RVLT_Delayed_Predicted_0,
    RVLT_Delayed_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.534,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.739,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.802,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.890,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.787,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.005,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.869,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.135,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.559,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.571,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.815,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.721,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.859,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.793,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.901,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.890
    ),
    RVLT_Delayed_Normed_0 = RVLT_Delayed_Z_0*3 +10,
    RVLT_Delayed_Normed_0 = if_else(RVLT_Delayed_Normed_0 < 0, 0.01, RVLT_Delayed_Normed_0),
    
    #Animal Fluency
    Animal_Fluency_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.132 - 0.095*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 28.923 - 0.157*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 32.513 - 0.202*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 31.143 - 0.168*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.433 - 0.114*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 29.912 - 0.181*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 30.764 - 0.178*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 32.003 - 0.186*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 26.034 - 0.152*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 33.358 - 0.241*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 36.511 - 0.277*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 30.193 - 0.179*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.460 - 0.089*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.355 - 0.070*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 30.881 - 0.205*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 29.961 - 0.180*Age_0
    ),
    Animal_Fluency_Residual_0 = Animal_Fluency_Lenient_0 - Animal_Fluency_Predicted_0,
    Animal_Fluency_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.145,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.348,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.163,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.354,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.665,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.728,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.176,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.369,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/3.911,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.889,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/5.061,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.869,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.178,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.321,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.468,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.940
    ),
    Animal_Fluency_Normed_0 = Animal_Fluency_Z_0*3 +10,
    Animal_Fluency_Normed_0 = if_else(Animal_Fluency_Normed_0 < 0, 0.01, Animal_Fluency_Normed_0),
    
    #Mental Alteration Test    
    MAT_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 33.295 - 0.161*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 34.074 - 0.123*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 41.488 - 0.219*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 40.573 - 0.190*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 39.102 - 0.251*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 41.657 - 0.246*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 36.877 - 0.168*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 38.849 - 0.188*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 36.630 - 0.252*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.784 - 0.181*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 51.105 - 0.381*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 44.106 - 0.257*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 34.814 - 0.214*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.756 - 0.202*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 47.024 - 0.315*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 41.717 - 0.234*Age_0
    ),
    MAT_Residual_0 = MAT_Score_0 - MAT_Predicted_0,
    MAT_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.602,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.702,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.490,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.727,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.080,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.139,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.915,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.979,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.589,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.234,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.314,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.609,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.803,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.079,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.451,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.734
    ),
    MAT_Normed_0 = MAT_Z_0*3 +10,
    MAT_Normed_0 = if_else(MAT_Normed_0 < 0, 0.01, MAT_Normed_0),
    
    #Global Cognition Composite Score
    Global_Composite_0 = RVLT_Immediate_Z_0 + RVLT_Delayed_Z_0 + Animal_Fluency_Z_0 + MAT_Z_0,
    
    #FU1 Scores
    
    #RVLT Immediate Score
    RVLT_Immediate_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.768 - 0.050*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.449 - 0.036*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 10.095 - 0.073*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.686 - 0.064*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 8.077 - 0.043*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 9.806 - 0.059*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 9.161 - 0.047*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.804 - 0.053*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 5.666 - 0.025*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.953 - 0.067*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 7.662 - 0.039*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 8.829 - 0.057*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 6.976 - 0.031*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.667 - 0.045*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 9.502 - 0.061*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 9.013 - 0.047*Age_1
    ),
    RVLT_Immediate_Residual_1 = RVLT_Immediate_Score_1 - RVLT_Immediate_Predicted_1,
    RVLT_Immediate_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.471,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.525,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.611,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.675,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.528,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.643,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.694,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.802,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.290,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.473,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.913,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.641,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.623,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.595,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.605,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.715
    ),
    RVLT_Immediate_Normed_1 = RVLT_Immediate_Z_1*3 + 10,
    RVLT_Immediate_Normed_1 = if_else(RVLT_Immediate_Normed_1 < 0, 0.01, RVLT_Immediate_Normed_1),
    
    
    #RVLT Delayed Score
    RVLT_Delayed_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.628 - 0.062*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.851 - 0.058*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.289 - 0.076*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.165 - 0.070*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 7.163 - 0.055*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 8.115 - 0.063*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.151 - 0.059*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.844 - 0.066*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 4.802 - 0.036*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 8.219 - 0.083*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.721 - 0.097*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 7.048 - 0.055*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.280 - 0.044*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.999 - 0.042*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.081 - 0.074*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 8.712 - 0.066*Age_1
    ),
    RVLT_Delayed_Residual_1 = RVLT_Delayed_Score_1 - RVLT_Delayed_Predicted_1,
    RVLT_Delayed_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.534,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.739,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.802,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.890,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.787,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.005,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.869,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.135,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.559,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.571,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.815,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.721,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.859,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.793,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.901,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.890
    ),
    RVLT_Delayed_Normed_1 = RVLT_Delayed_Z_1*3 + 10,
    RVLT_Delayed_Normed_1 = if_else(RVLT_Delayed_Normed_1 < 0, 0.01, RVLT_Delayed_Normed_1),
    
    #Animal Fluency
    Animal_Fluency_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.132 - 0.095*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 28.923 - 0.157*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 32.513 - 0.202*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 31.143 - 0.168*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.433 - 0.114*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 29.912 - 0.181*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 30.764 - 0.178*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 32.003 - 0.186*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 26.034 - 0.152*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 33.358 - 0.241*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 36.511 - 0.277*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 30.193 - 0.179*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.460 - 0.089*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.355 - 0.070*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 30.881 - 0.205*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 29.961 - 0.180*Age_1
    ),
    Animal_Fluency_Residual_1 = Animal_Fluency_Lenient_1 - Animal_Fluency_Predicted_1,
    Animal_Fluency_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.145,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.348,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.163,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.354,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.665,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.728,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.176,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.369,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/3.911,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.889,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/5.061,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.869,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.178,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.321,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.468,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.940
    ),
    Animal_Fluency_Normed_1 = Animal_Fluency_Z_1*3 + 10,
    Animal_Fluency_Normed_1 = if_else(Animal_Fluency_Normed_1 < 0, 0.01, Animal_Fluency_Normed_1),
    
    #Mental Alteration Test
    MAT_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 33.295 - 0.161*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 34.074 - 0.123*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 41.488 - 0.219*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 40.573 - 0.190*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 39.102 - 0.251*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 41.657 - 0.246*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 36.877 - 0.168*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 38.849 - 0.188*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 36.630 - 0.252*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.784 - 0.181*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 51.105 - 0.381*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 44.106 - 0.257*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 34.814 - 0.214*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.756 - 0.202*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 47.024 - 0.315*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 41.717 - 0.234*Age_1
    ),
    MAT_Residual_1 = MAT_Score_1 - MAT_Predicted_1,
    MAT_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.602,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.702,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.490,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.727,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.080,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.139,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.915,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.979,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.589,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.234,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.314,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.609,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.803,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Predicted_1/7.079,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.451,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.734
    ),
    MAT_Normed_1 = MAT_Z_1*3 + 10,
    MAT_Normed_1 = if_else(MAT_Normed_1 < 0, 0.01, MAT_Normed_1),
    
    #Global Cognition Composite Score
    Global_Composite_1 = RVLT_Immediate_Z_1 + RVLT_Delayed_Z_1 + Animal_Fluency_Z_1 + MAT_Z_1,
    
    
    #FU2 Scores
    
    #RVLT Immediate Score
    RVLT_Immediate_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.768 - 0.050*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.449 - 0.036*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 10.095 - 0.073*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.686 - 0.064*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 8.077 - 0.043*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 9.806 - 0.059*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 9.161 - 0.047*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.804 - 0.053*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 5.666 - 0.025*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.953 - 0.067*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 7.662 - 0.039*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 8.829 - 0.057*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 6.976 - 0.031*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.667 - 0.045*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 9.502 - 0.061*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 9.013 - 0.047*Age_2
    ),
    RVLT_Immediate_Residual_2 = RVLT_Immediate_Score_2 - RVLT_Immediate_Predicted_2,
    RVLT_Immediate_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.471,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.525,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.611,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.675,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.528,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.643,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.694,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.802,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.290,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.473,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.913,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.641,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.623,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.595,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.605,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.715
    ),
    RVLT_Immediate_Normed_2 = RVLT_Immediate_Z_2*3 + 10,
    RVLT_Immediate_Normed_2 = if_else(RVLT_Immediate_Normed_2 < 0, 0.01, RVLT_Immediate_Normed_2),
    
    #RVLT Delayed Score
    RVLT_Delayed_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.628 - 0.062*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.851 - 0.058*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.289 - 0.076*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.165 - 0.070*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 7.163 - 0.055*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 8.115 - 0.063*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.151 - 0.059*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.844 - 0.066*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 4.802 - 0.036*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 8.219 - 0.083*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.721 - 0.097*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 7.048 - 0.055*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.280 - 0.044*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.999 - 0.042*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.081 - 0.074*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 8.712 - 0.066*Age_2
    ),
    RVLT_Delayed_Residual_2 = RVLT_Delayed_Score_2 - RVLT_Delayed_Predicted_2,
    RVLT_Delayed_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.534,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.739,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.802,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.890,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.787,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.005,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.869,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.135,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.559,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.571,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.815,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.721,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.859,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.793,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.901,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.890
    ),
    RVLT_Delayed_Normed_2 = RVLT_Delayed_Z_2*3 + 10,
    RVLT_Delayed_Normed_2 = if_else(RVLT_Delayed_Normed_2 < 0, 0.01, RVLT_Delayed_Normed_2),
    
    #Animal Fluency
    Animal_Fluency_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.132 - 0.095*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 28.923 - 0.157*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 32.513 - 0.202*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 31.143 - 0.168*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.433 - 0.114*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 29.912 - 0.181*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 30.764 - 0.178*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 32.003 - 0.186*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 26.034 - 0.152*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 33.358 - 0.241*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 36.511 - 0.277*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 30.193 - 0.179*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.460 - 0.089*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.355 - 0.070*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 30.881 - 0.205*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 29.961 - 0.180*Age_2
    ),
    Animal_Fluency_Residual_2 = Animal_Fluency_Lenient_2 - Animal_Fluency_Predicted_2,
    Animal_Fluency_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.145,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.348,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.163,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.354,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.665,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.728,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.176,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.369,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/3.911,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.889,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/5.061,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.869,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.178,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.321,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.468,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.940
    ),
    Animal_Fluency_Normed_2 = Animal_Fluency_Z_2*3 + 10,
    Animal_Fluency_Normed_2 = if_else(Animal_Fluency_Normed_2 < 0, 0.01, Animal_Fluency_Normed_2),
    
    #Mental Alteration Test
    MAT_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 33.295 - 0.161*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 34.074 - 0.123*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 41.488 - 0.219*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 40.573 - 0.190*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 39.102 - 0.251*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 41.657 - 0.246*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 36.877 - 0.168*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 38.849 - 0.188*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 36.630 - 0.252*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.784 - 0.181*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 51.105 - 0.381*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 44.106 - 0.257*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 34.814 - 0.214*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.756 - 0.202*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 47.024 - 0.315*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 41.717 - 0.234*Age_2
    ),
    MAT_Residual_2 = MAT_Score_2 - MAT_Predicted_2,
    MAT_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.602,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.702,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.490,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.727,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.080,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.139,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.915,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.979,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.589,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.234,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.314,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.609,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.803,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.079,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.451,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.734
    ),
    MAT_Normed_2 = MAT_Z_2*3 + 10,
    MAT_Normed_2 = if_else(MAT_Normed_2 < 0, 0.01, MAT_Normed_2),
    
    #Global Cognition Composite Score
    Global_Composite_2 = RVLT_Immediate_Z_2 + RVLT_Delayed_Z_2 + Animal_Fluency_Z_0 + MAT_Z_2
  )

Finally, we categorized participants as having their follow-up 2 data collected before or after the start of the COVID-19 pandemic.

Tracking.Adjusted_Final <- Tracking.Adjusted_Full %>%
  mutate(timestamp = ymd_hms(startdate_TRF2_2, tz = "EST"))
## Date in ISO8601 format; converting timezone from UTC to "EST".
start_time <- as.POSIXct("2020-03-11 00:00:00", tz = "EST")
Tracking.Adjusted_Final$Pandemic<-NA
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp>=start_time]<-"FU2 data collected after COVID-19"
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp<start_time]<-"FU2 data collected before COVID-19"

2.1) Flow-chart for participants

Full tracking cohort at baseline (N=21,241)

Track.Baseline.total <- Track.Baseline.Final %>%
  count()
print(Track.Baseline.total)
##       n
## 1 21241

Excluding tracking cohort participants lost to follow-up (N=14,697)

Track.Complete <- Track.Full %>%
  count()
print(Track.Complete)
##       n
## 1 14697

Baseline tracking cohort removing individuals who reported SCI or dementia at baseline, FU1, or FU2 (N=13,934)

Track.SCI_Dementia <- Track.Full %>%
  group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2) %>%
  count()
print(Track.SCI_Dementia)
## # A tibble: 40 × 7
## # Groups:   SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2 [40]
##    SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2     n
##    <chr> <chr>      <chr> <chr>      <chr> <chr>      <int>
##  1 No    No         No    No         No    No         13934
##  2 No    No         No    No         No    Yes            7
##  3 No    No         No    No         No    <NA>           7
##  4 No    No         No    No         Yes   No           143
##  5 No    No         No    No         Yes   Yes           18
##  6 No    No         No    No         Yes   <NA>           2
##  7 No    No         No    No         <NA>  No            15
##  8 No    No         No    No         <NA>  Yes            1
##  9 No    No         No    No         <NA>  <NA>         160
## 10 No    No         No    Yes        No    <NA>           3
## # ℹ 30 more rows

Baseline tracking cohort removing individuals with dementia or SCI and without full cog data (N=11,355)

Track.FullCogs <- Track.Full %>%
  group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2, 
           !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0)) %>%
  count()
print(Track.FullCogs)
## # A tibble: 105 × 11
## # Groups:   SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2,
## #   !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## #   !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0) [105]
##    SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2 `!is.na(MAT_Score_0)`
##    <chr> <chr>      <chr> <chr>      <chr> <chr>      <lgl>                
##  1 No    No         No    No         No    No         FALSE                
##  2 No    No         No    No         No    No         FALSE                
##  3 No    No         No    No         No    No         FALSE                
##  4 No    No         No    No         No    No         FALSE                
##  5 No    No         No    No         No    No         FALSE                
##  6 No    No         No    No         No    No         FALSE                
##  7 No    No         No    No         No    No         FALSE                
##  8 No    No         No    No         No    No         FALSE                
##  9 No    No         No    No         No    No         TRUE                 
## 10 No    No         No    No         No    No         TRUE                 
## # ℹ 95 more rows
## # ℹ 4 more variables: `!is.na(RVLT_Immediate_Score_0)` <lgl>,
## #   `!is.na(RVLT_Delayed_Score_0)` <lgl>,
## #   `!is.na(Animal_Fluency_Strict_0)` <lgl>, n <int>

Final sample size (N=11,355) with complete cognitive data at FU1 grouped by Pandemic cohort

Track.FullCogs2 <- Tracking.Adjusted_Final %>%
  group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
           !is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1), !is.na(RVLT_Delayed_Score_1),!is.na(Animal_Fluency_Strict_1), Pandemic) %>%
  count()
print(Track.FullCogs2)
## # A tibble: 29 × 10
## # Groups:   !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## #   !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## #   !is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1),
## #   !is.na(RVLT_Delayed_Score_1), !is.na(Animal_Fluency_Strict_1), Pandemic
## #   [29]
##    `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
##    <lgl>                 <lgl>                            <lgl>                 
##  1 TRUE                  TRUE                             TRUE                  
##  2 TRUE                  TRUE                             TRUE                  
##  3 TRUE                  TRUE                             TRUE                  
##  4 TRUE                  TRUE                             TRUE                  
##  5 TRUE                  TRUE                             TRUE                  
##  6 TRUE                  TRUE                             TRUE                  
##  7 TRUE                  TRUE                             TRUE                  
##  8 TRUE                  TRUE                             TRUE                  
##  9 TRUE                  TRUE                             TRUE                  
## 10 TRUE                  TRUE                             TRUE                  
## # ℹ 19 more rows
## # ℹ abbreviated name: ¹​`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## #   `!is.na(MAT_Score_1)` <lgl>, `!is.na(RVLT_Immediate_Score_1)` <lgl>,
## #   `!is.na(RVLT_Delayed_Score_1)` <lgl>,
## #   `!is.na(Animal_Fluency_Strict_1)` <lgl>, Pandemic <chr>, n <int>

Final sample size (N=11,355) with complete cognitive data at FU2 grouped by Pandemic cohort

Track.FullCogs3 <- Tracking.Adjusted_Final %>%
  group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
           !is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2), !is.na(RVLT_Delayed_Score_2),!is.na(Animal_Fluency_Strict_2), Pandemic) %>%
  count()
print(Track.FullCogs3)
## # A tibble: 25 × 10
## # Groups:   !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## #   !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## #   !is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2),
## #   !is.na(RVLT_Delayed_Score_2), !is.na(Animal_Fluency_Strict_2), Pandemic
## #   [25]
##    `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
##    <lgl>                 <lgl>                            <lgl>                 
##  1 TRUE                  TRUE                             TRUE                  
##  2 TRUE                  TRUE                             TRUE                  
##  3 TRUE                  TRUE                             TRUE                  
##  4 TRUE                  TRUE                             TRUE                  
##  5 TRUE                  TRUE                             TRUE                  
##  6 TRUE                  TRUE                             TRUE                  
##  7 TRUE                  TRUE                             TRUE                  
##  8 TRUE                  TRUE                             TRUE                  
##  9 TRUE                  TRUE                             TRUE                  
## 10 TRUE                  TRUE                             TRUE                  
## # ℹ 15 more rows
## # ℹ abbreviated name: ¹​`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## #   `!is.na(MAT_Score_2)` <lgl>, `!is.na(RVLT_Immediate_Score_2)` <lgl>,
## #   `!is.na(RVLT_Delayed_Score_2)` <lgl>,
## #   `!is.na(Animal_Fluency_Strict_2)` <lgl>, Pandemic <chr>, n <int>

Final sample size (N=11,355) and number of participants with PASE score at baseline (N=9,181)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(PASE_TOTAL_0), Pandemic) %>%
  count()
print(PASEBL)
## # A tibble: 4 × 3
## # Groups:   !is.na(PASE_TOTAL_0), Pandemic [4]
##   `!is.na(PASE_TOTAL_0)` Pandemic                               n
##   <lgl>                  <chr>                              <int>
## 1 FALSE                  FU2 data collected after COVID-19   1027
## 2 FALSE                  FU2 data collected before COVID-19  1147
## 3 TRUE                   FU2 data collected after COVID-19   4154
## 4 TRUE                   FU2 data collected before COVID-19  5027

Final sample size (N=11,355) and number of participants with PASE score at baseline or FU1 (N=1,945)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1),Pandemic) %>%
  count()
print(PASEBL)
## # A tibble: 8 × 4
## # Groups:   !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), Pandemic [8]
##   `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` Pandemic                       n
##   <lgl>                  <lgl>                  <chr>                      <int>
## 1 FALSE                  FALSE                  FU2 data collected after …   882
## 2 FALSE                  FALSE                  FU2 data collected before…  1016
## 3 FALSE                  TRUE                   FU2 data collected after …   145
## 4 FALSE                  TRUE                   FU2 data collected before…   131
## 5 TRUE                   FALSE                  FU2 data collected after …  3226
## 6 TRUE                   FALSE                  FU2 data collected before…  4010
## 7 TRUE                   TRUE                   FU2 data collected after …   928
## 8 TRUE                   TRUE                   FU2 data collected before…  1017

Final sample size (N=11,355) and number of participants with PASE score at baseline, FU1, or FU2 (N=762)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), , !is.na(PASE_TOTAL_2),Pandemic) %>%
  count()
print(PASEBL)
## # A tibble: 16 × 5
## # Groups:   !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), !is.na(PASE_TOTAL_2),
## #   Pandemic [16]
##    `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` `!is.na(PASE_TOTAL_2)` Pandemic
##    <lgl>                  <lgl>                  <lgl>                  <chr>   
##  1 FALSE                  FALSE                  FALSE                  FU2 dat…
##  2 FALSE                  FALSE                  FALSE                  FU2 dat…
##  3 FALSE                  FALSE                  TRUE                   FU2 dat…
##  4 FALSE                  FALSE                  TRUE                   FU2 dat…
##  5 FALSE                  TRUE                   FALSE                  FU2 dat…
##  6 FALSE                  TRUE                   FALSE                  FU2 dat…
##  7 FALSE                  TRUE                   TRUE                   FU2 dat…
##  8 FALSE                  TRUE                   TRUE                   FU2 dat…
##  9 TRUE                   FALSE                  FALSE                  FU2 dat…
## 10 TRUE                   FALSE                  FALSE                  FU2 dat…
## 11 TRUE                   FALSE                  TRUE                   FU2 dat…
## 12 TRUE                   FALSE                  TRUE                   FU2 dat…
## 13 TRUE                   TRUE                   FALSE                  FU2 dat…
## 14 TRUE                   TRUE                   FALSE                  FU2 dat…
## 15 TRUE                   TRUE                   TRUE                   FU2 dat…
## 16 TRUE                   TRUE                   TRUE                   FU2 dat…
## # ℹ 1 more variable: n <int>

Final sample size (N=11,355) and number of participants with sleep data at baseline (N=11,334)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(RSTLS_Sleep_0),Pandemic) %>%
  count()
print(PASEBL)
## # A tibble: 4 × 3
## # Groups:   !is.na(RSTLS_Sleep_0), Pandemic [4]
##   `!is.na(RSTLS_Sleep_0)` Pandemic                               n
##   <lgl>                   <chr>                              <int>
## 1 FALSE                   FU2 data collected after COVID-19     11
## 2 FALSE                   FU2 data collected before COVID-19    10
## 3 TRUE                    FU2 data collected after COVID-19   5170
## 4 TRUE                    FU2 data collected before COVID-19  6164

Final sample size (N=11,355) and number of participants with sleep data at baseline or FU1 (N=11,304)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),Pandemic) %>%
  count()
print(PASEBL)
## # A tibble: 6 × 4
## # Groups:   !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), Pandemic [6]
##   `!is.na(RSTLS_Sleep_0)` `!is.na(RSTLS_Sleep_1)` Pandemic                     n
##   <lgl>                   <lgl>                   <chr>                    <int>
## 1 FALSE                   TRUE                    FU2 data collected afte…    11
## 2 FALSE                   TRUE                    FU2 data collected befo…    10
## 3 TRUE                    FALSE                   FU2 data collected afte…     7
## 4 TRUE                    FALSE                   FU2 data collected befo…    23
## 5 TRUE                    TRUE                    FU2 data collected afte…  5163
## 6 TRUE                    TRUE                    FU2 data collected befo…  6141

Final sample size (N=11,355) and number of participants with sleep data at baseline, FU1, or FU2 (N=11,270)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), , !is.na(RSTLS_Sleep_2),Pandemic) %>%
  count()
print(PASEBL)
## # A tibble: 8 × 5
## # Groups:   !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),
## #   !is.na(RSTLS_Sleep_2), Pandemic [8]
##   `!is.na(RSTLS_Sleep_0)` !is.na(RSTLS_Sleep_1…¹ !is.na(RSTLS_Sleep_2…² Pandemic
##   <lgl>                   <lgl>                  <lgl>                  <chr>   
## 1 FALSE                   TRUE                   TRUE                   FU2 dat…
## 2 FALSE                   TRUE                   TRUE                   FU2 dat…
## 3 TRUE                    FALSE                  TRUE                   FU2 dat…
## 4 TRUE                    FALSE                  TRUE                   FU2 dat…
## 5 TRUE                    TRUE                   FALSE                  FU2 dat…
## 6 TRUE                    TRUE                   FALSE                  FU2 dat…
## 7 TRUE                    TRUE                   TRUE                   FU2 dat…
## 8 TRUE                    TRUE                   TRUE                   FU2 dat…
## # ℹ abbreviated names: ¹​`!is.na(RSTLS_Sleep_1)`, ²​`!is.na(RSTLS_Sleep_2)`
## # ℹ 1 more variable: n <int>

#2.2) Participants with positive COVID test

Sample of participants w/ COVID data

Track.Full.COVID.Final <- subset(Track.Full.COVID, SCI_0=="No" & Dementia_0=="No" & SCI_1=="No" & Dementia_1 =="No" & 
                       SCI_2=="No" & Dementia_2 == "No" & !is.na(Animal_Fluency_Strict_0) & !is.na(MAT_Score_0) & !is.na(Education4_0) &
                       !is.na(RVLT_Immediate_Score_0) & !is.na(RVLT_Delayed_Score_0))

Participants w/ or w/out positive COVID-19 test (Yes = 1; No = 2; Results not available = 3; 8 = Don’t know; -9999 = No data)

Covid.test <- Track.Full.COVID.Final %>%
  group_by((SYM_TESTPOS_COVID)) %>%
  count()
print(Covid.test)
## # A tibble: 5 × 2
## # Groups:   (SYM_TESTPOS_COVID) [5]
##   `(SYM_TESTPOS_COVID)`     n
##                   <int> <int>
## 1                -99999  7397
## 2                     1     7
## 3                     2    95
## 4                     3     7
## 5                     8     1

3) Participant characteristics at baseline

Create factor variables for PASE sedentary behaviour and sleep score

BL.data<-Tracking.Adjusted_Final
BL.data$PASE_Q1B_0 <- as.factor(ifelse(BL.data$PASE_Q1B_0==10, 1, 0))
BL.data$RSTLS_Sleep_0 <- as.factor(BL.data$RSTLS_Sleep_0)

Final baseline sample (N= 11,355)

Baseline<-dput(names(BL.data[c(5,4,14,12,6,7,8,9,10,11,15,18,19,21,98,102,106,110,29,31,27,24,13,22,23)]))
## c("Age_0", "Sex_0", "BMI_0", "Ethnicity_0", "Relationship_status_0", 
## "Education4_0", "Income_Level_0", "Living_status_0", "Alcohol_0", 
## "Smoking_Status_0", "CESD_10_0", "Anxiety_0", "Mood_Disord_0", 
## "Chronic_conditions_0", "RVLT_Immediate_Normed_0", "RVLT_Delayed_Normed_0", 
## "Animal_Fluency_Normed_0", "MAT_Normed_0", "RVLT_Immediate_Lang_0", 
## "RVLT_Delayed_Lang_0", "MAT_Lang_0", "Animal_Fluency_Lang_0", 
## "PASE_TOTAL_0", "PASE_Q1B_0", "RSTLS_Sleep_0")
Table1_Final<-CreateTableOne(vars=Baseline, data=BL.data)
print(Table1_Final,contDigits=2,missing=TRUE,quote=TRUE)
##                                               ""
##  ""                                            "Overall"        "Missing"
##   "n"                                          " 11355"         "    "   
##   "Age_0 (mean (SD))"                          " 61.62 (10.08)" " 0.0"   
##   "Sex_0 = M (%)"                              "  5467 (48.1) " " 0.0"   
##   "BMI_0 (mean (SD))"                          " 27.50 (5.10)"  " 0.5"   
##   "Ethnicity_0 = White (%)"                    " 11043 (97.3) " " 0.0"   
##   "Relationship_status_0 (%)"                  " "              " 0.0"   
##   "   Divorced"                                "   997 ( 8.8) " "    "   
##   "   Married"                                 "  8210 (72.3) " "    "   
##   "   Separated"                               "   290 ( 2.6) " "    "   
##   "   Single"                                  "   852 ( 7.5) " "    "   
##   "   Widowed"                                 "  1001 ( 8.8) " "    "   
##   "Education4_0 (%)"                           " "              " 0.0"   
##   "   College Degree or Higher"                "  8322 (73.3) " "    "   
##   "   High School Diploma"                     "  1449 (12.8) " "    "   
##   "   Less than High School Diploma"           "   749 ( 6.6) " "    "   
##   "   Some College"                            "   835 ( 7.4) " "    "   
##   "Income_Level_0 (%)"                         " "              " 3.5"   
##   "   <$20k"                                   "  1764 (16.1) " "    "   
##   "   >$150k"                                  "   428 ( 3.9) " "    "   
##   "   $100-150k"                               "   836 ( 7.6) " "    "   
##   "   $20-50k"                                 "  4349 (39.7) " "    "   
##   "   $50-100k"                                "  3584 (32.7) " "    "   
##   "Living_status_0 (%)"                        " "              " 0.0"   
##   "   Apartment/Condo/Townhome"                "  1342 (11.8) " "    "   
##   "   Assisted Living"                         "    61 ( 0.5) " "    "   
##   "   House"                                   "  9852 (86.8) " "    "   
##   "   Other"                                   "   100 ( 0.9) " "    "   
##   "Alcohol_0 (%)"                              " "              " 3.1"   
##   "   Non-drinker"                             "  1154 (10.5) " "    "   
##   "   Occasional drinker"                      "  1729 (15.7) " "    "   
##   "   Regular drinker (at least once a month)" "  8124 (73.8) " "    "   
##   "Smoking_Status_0 (%)"                       " "              " 0.4"   
##   "   Daily Smoker"                            "   744 ( 6.6) " "    "   
##   "   Former Smoker"                           "  6830 (60.4) " "    "   
##   "   Never Smoked"                            "  3541 (31.3) " "    "   
##   "   Occasional Smoker"                       "   189 ( 1.7) " "    "   
##   "CESD_10_0 (mean (SD))"                      "  4.96 (4.36)"  " 0.3"   
##   "Anxiety_0 = Yes (%)"                        "   718 ( 6.3) " " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    "  1508 (13.3) " " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           "  2.77 (2.26)"  " 3.8"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.44 (3.81)"  " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.71 (3.71)"  " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.21 (3.43)"  " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   "  9.95 (3.46)"  " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "  1962 (17.3) " " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "  1962 (17.3) " " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "  1962 (17.3) " " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "  1962 (17.3) " " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "168.93 (78.96)" "19.1"   
##   "PASE_Q1B_0 = 1 (%)"                         "  4305 (38.6) " " 1.8"   
##   "RSTLS_Sleep_0 = 1 (%)"                      "  3741 (33.0) " " 0.2"

Final baseline sample stratified by whether FU2 data was collected before (N= 6,174) or after (N= 5,181) the start of the COVID-19 pandemic

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.data)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  5181"                           
##   "Age_0 (mean (SD))"                          " 60.29 (10.54)"                   
##   "Sex_0 = M (%)"                              "  2856 (55.1) "                   
##   "BMI_0 (mean (SD))"                          " 27.52 (4.94)"                    
##   "Ethnicity_0 = White (%)"                    "  5025 (97.0) "                   
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   408 ( 7.9) "                   
##   "   Married"                                 "  3841 (74.2) "                   
##   "   Separated"                               "   150 ( 2.9) "                   
##   "   Single"                                  "   383 ( 7.4) "                   
##   "   Widowed"                                 "   397 ( 7.7) "                   
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  3605 (69.6) "                   
##   "   High School Diploma"                     "   776 (15.0) "                   
##   "   Less than High School Diploma"           "   411 ( 7.9) "                   
##   "   Some College"                            "   389 ( 7.5) "                   
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   783 (15.6) "                   
##   "   >$150k"                                  "   239 ( 4.8) "                   
##   "   $100-150k"                               "   426 ( 8.5) "                   
##   "   $20-50k"                                 "  1874 (37.3) "                   
##   "   $50-100k"                                "  1700 (33.9) "                   
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   579 (11.2) "                   
##   "   Assisted Living"                         "    24 ( 0.5) "                   
##   "   House"                                   "  4541 (87.6) "                   
##   "   Other"                                   "    37 ( 0.7) "                   
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   512 (10.2) "                   
##   "   Occasional drinker"                      "   784 (15.6) "                   
##   "   Regular drinker (at least once a month)" "  3731 (74.2) "                   
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   361 ( 7.0) "                   
##   "   Former Smoker"                           "  3128 (60.6) "                   
##   "   Never Smoked"                            "  1579 (30.6) "                   
##   "   Occasional Smoker"                       "    90 ( 1.7) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.08 (4.47)"                    
##   "Anxiety_0 = Yes (%)"                        "   335 ( 6.5) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   698 (13.5) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  2.60 (2.22)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.13 (3.76)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.45 (3.69)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.15 (3.45)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.82 (3.46)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "  1319 (25.5) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "  1319 (25.5) "                   
##   "MAT_Lang_0 = French (%)"                    "  1319 (25.5) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "  1319 (25.5) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "179.56 (81.39)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "  1875 (37.2) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "  1710 (33.1) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  6174"                            
##   "Age_0 (mean (SD))"                          " 62.74 (9.53)"                     
##   "Sex_0 = M (%)"                              "  2611 (42.3) "                    
##   "BMI_0 (mean (SD))"                          " 27.48 (5.24)"                     
##   "Ethnicity_0 = White (%)"                    "  6018 (97.5) "                    
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   589 ( 9.5) "                    
##   "   Married"                                 "  4369 (70.8) "                    
##   "   Separated"                               "   140 ( 2.3) "                    
##   "   Single"                                  "   469 ( 7.6) "                    
##   "   Widowed"                                 "   604 ( 9.8) "                    
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  4717 (76.4) "                    
##   "   High School Diploma"                     "   673 (10.9) "                    
##   "   Less than High School Diploma"           "   338 ( 5.5) "                    
##   "   Some College"                            "   446 ( 7.2) "                    
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   981 (16.5) "                    
##   "   >$150k"                                  "   189 ( 3.2) "                    
##   "   $100-150k"                               "   410 ( 6.9) "                    
##   "   $20-50k"                                 "  2475 (41.7) "                    
##   "   $50-100k"                                "  1884 (31.7) "                    
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   763 (12.4) "                    
##   "   Assisted Living"                         "    37 ( 0.6) "                    
##   "   House"                                   "  5311 (86.0) "                    
##   "   Other"                                   "    63 ( 1.0) "                    
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   642 (10.7) "                    
##   "   Occasional drinker"                      "   945 (15.8) "                    
##   "   Regular drinker (at least once a month)" "  4393 (73.5) "                    
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   383 ( 6.2) "                    
##   "   Former Smoker"                           "  3702 (60.2) "                    
##   "   Never Smoked"                            "  1962 (31.9) "                    
##   "   Occasional Smoker"                       "    99 ( 1.6) "                    
##   "CESD_10_0 (mean (SD))"                      "  4.85 (4.27)"                     
##   "Anxiety_0 = Yes (%)"                        "   383 ( 6.2) "                    
##   "Mood_Disord_0 = Yes (%)"                    "   810 (13.1) "                    
##   "Chronic_conditions_0 (mean (SD))"           "  2.91 (2.29)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.70 (3.84)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.93 (3.72)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.26 (3.41)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.06 (3.46)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   643 (10.4) "                    
##   "RVLT_Delayed_Lang_0 = French (%)"           "   643 (10.4) "                    
##   "MAT_Lang_0 = French (%)"                    "   643 (10.4) "                    
##   "Animal_Fluency_Lang_0 = French (%)"         "   643 (10.4) "                    
##   "PASE_TOTAL_0 (mean (SD))"                   "160.16 (75.79)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "  2430 (39.7) "                    
##   "RSTLS_Sleep_0 = 1 (%)"                      "  2031 (32.9) "                    
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "<0.001" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.688" ""     " 0.5"   
##   "Ethnicity_0 = White (%)"                    " 0.130" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  "<0.001" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         "<0.001" ""     " 3.5"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.043" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.584" ""     " 3.1"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.219" ""     " 0.4"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.005" ""     " 0.3"   
##   "Anxiety_0 = Yes (%)"                        " 0.589" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.604" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           "<0.001" ""     " 3.8"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.087" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   "<0.001" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "19.1"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.008" ""     " 1.8"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.903" ""     " 0.2"

Stratify Results By Age, Sex, and Pandemic Status

BL.data$Age_sex<-NA
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "M"]<-"Males 45-64"
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "F"]<-"Females 45-64"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "M"]<-"Males 65+"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "F"]<-"Females 65+"

BL.MalesYoung<-subset(BL.data, Age_sex=="Males 45-64")
BL.FemalesYoung<-subset(BL.data, Age_sex=="Females 45-64")
BL.MalesOld<-subset(BL.data, Age_sex=="Males 65+")
BL.FemalesOld<-subset(BL.data, Age_sex=="Females 65+")

Males 45-64

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  2032"                           
##   "Age_0 (mean (SD))"                          " 54.12 (5.36)"                    
##   "Sex_0 = M (%)"                              "  2032 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.98 (4.44)"                    
##   "Ethnicity_0 = White (%)"                    "  1947 ( 95.8) "                  
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   126 (  6.2) "                  
##   "   Married"                                 "  1656 ( 81.5) "                  
##   "   Separated"                               "    64 (  3.2) "                  
##   "   Single"                                  "   163 (  8.0) "                  
##   "   Widowed"                                 "    22 (  1.1) "                  
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  1555 ( 76.5) "                  
##   "   High School Diploma"                     "   248 ( 12.2) "                  
##   "   Less than High School Diploma"           "    95 (  4.7) "                  
##   "   Some College"                            "   134 (  6.6) "                  
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   123 (  6.2) "                  
##   "   >$150k"                                  "   188 (  9.4) "                  
##   "   $100-150k"                               "   297 ( 14.9) "                  
##   "   $20-50k"                                 "   517 ( 25.9) "                  
##   "   $50-100k"                                "   873 ( 43.7) "                  
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   146 (  7.2) "                  
##   "   Assisted Living"                         "     2 (  0.1) "                  
##   "   House"                                   "  1872 ( 92.1) "                  
##   "   Other"                                   "    12 (  0.6) "                  
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   174 (  8.7) "                  
##   "   Occasional drinker"                      "   223 ( 11.2) "                  
##   "   Regular drinker (at least once a month)" "  1597 ( 80.1) "                  
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   168 (  8.3) "                  
##   "   Former Smoker"                           "  1211 ( 59.8) "                  
##   "   Never Smoked"                            "   605 ( 29.9) "                  
##   "   Occasional Smoker"                       "    40 (  2.0) "                  
##   "CESD_10_0 (mean (SD))"                      "  4.82 (4.40)"                    
##   "Anxiety_0 = Yes (%)"                        "   100 (  4.9) "                  
##   "Mood_Disord_0 = Yes (%)"                    "   257 ( 12.7) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  1.91 (1.76)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "  9.73 (3.52)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.02 (3.56)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.24 (3.55)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.75 (3.41)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   485 ( 23.9) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   485 ( 23.9) "                  
##   "MAT_Lang_0 = French (%)"                    "   485 ( 23.9) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   485 ( 23.9) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "217.41 (81.01)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   630 ( 31.9) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   634 ( 31.2) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  1424"                            
##   "Age_0 (mean (SD))"                          " 56.68 (5.24)"                     
##   "Sex_0 = M (%)"                              "  1424 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 28.10 (4.65)"                     
##   "Ethnicity_0 = White (%)"                    "  1381 ( 97.0) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "    73 (  5.1) "                   
##   "   Married"                                 "  1152 ( 81.0) "                   
##   "   Separated"                               "    43 (  3.0) "                   
##   "   Single"                                  "   127 (  8.9) "                   
##   "   Widowed"                                 "    28 (  2.0) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  1125 ( 79.0) "                   
##   "   High School Diploma"                     "   147 ( 10.3) "                   
##   "   Less than High School Diploma"           "    50 (  3.5) "                   
##   "   Some College"                            "   102 (  7.2) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "    94 (  6.8) "                   
##   "   >$150k"                                  "    92 (  6.6) "                   
##   "   $100-150k"                               "   207 ( 14.9) "                   
##   "   $20-50k"                                 "   414 ( 29.7) "                   
##   "   $50-100k"                                "   585 ( 42.0) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   122 (  8.6) "                   
##   "   Assisted Living"                         "     1 (  0.1) "                   
##   "   House"                                   "  1290 ( 90.6) "                   
##   "   Other"                                   "    11 (  0.8) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   131 (  9.3) "                   
##   "   Occasional drinker"                      "   136 (  9.7) "                   
##   "   Regular drinker (at least once a month)" "  1135 ( 81.0) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   105 (  7.4) "                   
##   "   Former Smoker"                           "   874 ( 61.7) "                   
##   "   Never Smoked"                            "   401 ( 28.3) "                   
##   "   Occasional Smoker"                       "    37 (  2.6) "                   
##   "CESD_10_0 (mean (SD))"                      "  4.51 (4.01)"                     
##   "Anxiety_0 = Yes (%)"                        "    68 (  4.8) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   144 ( 10.1) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  2.11 (1.75)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.40 (3.86)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.60 (3.77)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.31 (3.67)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.02 (3.38)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   147 ( 10.3) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "   147 ( 10.3) "                   
##   "MAT_Lang_0 = French (%)"                    "   147 ( 10.3) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "   147 ( 10.3) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "199.82 (78.37)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   510 ( 36.3) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   422 ( 29.7) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.437" ""     " 0.2"   
##   "Ethnicity_0 = White (%)"                    " 0.091" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.132" ""     " 0.1"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           " 0.096" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.010" ""     " 1.9"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.428" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.343" ""     " 1.7"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.317" ""     " 0.4"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.032" ""     " 0.2"   
##   "Anxiety_0 = Yes (%)"                        " 0.905" ""     " 0.0"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.026" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.001" ""     " 2.7"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.576" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.022" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "17.5"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.008" ""     " 2.3"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.348" ""     " 0.1"

Females 45-64

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  1498"                           
##   "Age_0 (mean (SD))"                          " 54.09 (5.34)"                    
##   "Sex_0 = F (%)"                              "  1498 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.18 (5.75)"                    
##   "Ethnicity_0 = White (%)"                    "  1460 ( 97.5) "                  
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   137 (  9.1) "                  
##   "   Married"                                 "  1107 ( 73.9) "                  
##   "   Separated"                               "    56 (  3.7) "                  
##   "   Single"                                  "   141 (  9.4) "                  
##   "   Widowed"                                 "    57 (  3.8) "                  
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  1064 ( 71.0) "                  
##   "   High School Diploma"                     "   248 ( 16.6) "                  
##   "   Less than High School Diploma"           "    65 (  4.3) "                  
##   "   Some College"                            "   121 (  8.1) "                  
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   320 ( 22.2) "                  
##   "   >$150k"                                  "    26 (  1.8) "                  
##   "   $100-150k"                               "    71 (  4.9) "                  
##   "   $20-50k"                                 "   550 ( 38.2) "                  
##   "   $50-100k"                                "   473 ( 32.8) "                  
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   119 (  7.9) "                  
##   "   Assisted Living"                         "     1 (  0.1) "                  
##   "   House"                                   "  1371 ( 91.5) "                  
##   "   Other"                                   "     7 (  0.5) "                  
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   140 (  9.6) "                  
##   "   Occasional drinker"                      "   283 ( 19.4) "                  
##   "   Regular drinker (at least once a month)" "  1039 ( 71.1) "                  
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   138 (  9.3) "                  
##   "   Former Smoker"                           "   838 ( 56.2) "                  
##   "   Never Smoked"                            "   479 ( 32.1) "                  
##   "   Occasional Smoker"                       "    36 (  2.4) "                  
##   "CESD_10_0 (mean (SD))"                      "  5.68 (4.91)"                    
##   "Anxiety_0 = Yes (%)"                        "   159 ( 10.6) "                  
##   "Mood_Disord_0 = Yes (%)"                    "   298 ( 19.9) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  2.36 (2.05)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.13 (3.67)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.31 (3.50)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.21 (3.46)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.92 (3.32)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   396 ( 26.4) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   396 ( 26.4) "                  
##   "MAT_Lang_0 = French (%)"                    "   396 ( 26.4) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   396 ( 26.4) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "182.11 (73.93)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   507 ( 35.1) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   582 ( 39.0) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  2246"                            
##   "Age_0 (mean (SD))"                          " 55.97 (5.32)"                     
##   "Sex_0 = F (%)"                              "  2246 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 27.52 (6.16)"                     
##   "Ethnicity_0 = White (%)"                    "  2188 ( 97.4) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   250 ( 11.1) "                   
##   "   Married"                                 "  1620 ( 72.1) "                   
##   "   Separated"                               "    62 (  2.8) "                   
##   "   Single"                                  "   213 (  9.5) "                   
##   "   Widowed"                                 "   101 (  4.5) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  1747 ( 77.8) "                   
##   "   High School Diploma"                     "   244 ( 10.9) "                   
##   "   Less than High School Diploma"           "    82 (  3.7) "                   
##   "   Some College"                            "   173 (  7.7) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   483 ( 22.3) "                   
##   "   >$150k"                                  "    41 (  1.9) "                   
##   "   $100-150k"                               "   113 (  5.2) "                   
##   "   $20-50k"                                 "   847 ( 39.1) "                   
##   "   $50-100k"                                "   680 ( 31.4) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   217 (  9.7) "                   
##   "   Assisted Living"                         "     4 (  0.2) "                   
##   "   House"                                   "  2006 ( 89.3) "                   
##   "   Other"                                   "    19 (  0.8) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   224 ( 10.2) "                   
##   "   Occasional drinker"                      "   422 ( 19.3) "                   
##   "   Regular drinker (at least once a month)" "  1546 ( 70.5) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   187 (  8.3) "                   
##   "   Former Smoker"                           "  1200 ( 53.5) "                   
##   "   Never Smoked"                            "   810 ( 36.1) "                   
##   "   Occasional Smoker"                       "    45 (  2.0) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.26 (4.56)"                     
##   "Anxiety_0 = Yes (%)"                        "   180 (  8.0) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   423 ( 18.8) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  2.59 (2.14)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.65 (3.81)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.85 (3.61)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.36 (3.46)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.16 (3.38)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   304 ( 13.5) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "   304 ( 13.5) "                   
##   "MAT_Lang_0 = French (%)"                    "   304 ( 13.5) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "   304 ( 13.5) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "170.96 (72.48)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   830 ( 37.3) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   829 ( 37.0) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = F (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.084" ""     " 0.9"   
##   "Ethnicity_0 = White (%)"                    " 1.000" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.109" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.920" ""     " 3.7"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.105" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.817" ""     " 2.4"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.076" ""     " 0.3"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.008" ""     " 0.2"   
##   "Anxiety_0 = Yes (%)"                        " 0.007" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.443" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.001" ""     " 3.3"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.204" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.029" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "18.8"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.189" ""     " 2.0"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.231" ""     " 0.2"

Males 65+

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "   824"                           
##   "Age_0 (mean (SD))"                          " 73.50 (5.55)"                    
##   "Sex_0 = M (%)"                              "   824 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.23 (4.10)"                    
##   "Ethnicity_0 = White (%)"                    "   802 ( 97.3) "                  
##   "Relationship_status_0 (%)"                  "   "                              
##   "   Divorced"                                "    47 (  5.7) "                  
##   "   Married"                                 "   663 ( 80.6) "                  
##   "   Separated"                               "    18 (  2.2) "                  
##   "   Single"                                  "    33 (  4.0) "                  
##   "   Widowed"                                 "    62 (  7.5) "                  
##   "Education4_0 (%)"                           "   "                              
##   "   College Degree or Higher"                "   542 ( 65.8) "                  
##   "   High School Diploma"                     "   113 ( 13.7) "                  
##   "   Less than High School Diploma"           "   103 ( 12.5) "                  
##   "   Some College"                            "    66 (  8.0) "                  
##   "Income_Level_0 (%)"                         "   "                              
##   "   <$20k"                                   "    79 (  9.8) "                  
##   "   >$150k"                                  "    19 (  2.4) "                  
##   "   $100-150k"                               "    48 (  6.0) "                  
##   "   $20-50k"                                 "   408 ( 50.7) "                  
##   "   $50-100k"                                "   250 ( 31.1) "                  
##   "Living_status_0 (%)"                        "   "                              
##   "   Apartment/Condo/Townhome"                "   125 ( 15.2) "                  
##   "   Assisted Living"                         "     4 (  0.5) "                  
##   "   House"                                   "   690 ( 83.7) "                  
##   "   Other"                                   "     5 (  0.6) "                  
##   "Alcohol_0 (%)"                              "   "                              
##   "   Non-drinker"                             "    89 ( 11.1) "                  
##   "   Occasional drinker"                      "    92 ( 11.5) "                  
##   "   Regular drinker (at least once a month)" "   618 ( 77.3) "                  
##   "Smoking_Status_0 (%)"                       "   "                              
##   "   Daily Smoker"                            "    22 (  2.7) "                  
##   "   Former Smoker"                           "   620 ( 75.7) "                  
##   "   Never Smoked"                            "   171 ( 20.9) "                  
##   "   Occasional Smoker"                       "     6 (  0.7) "                  
##   "CESD_10_0 (mean (SD))"                      "  4.39 (3.83)"                    
##   "Anxiety_0 = Yes (%)"                        "    22 (  2.7) "                  
##   "Mood_Disord_0 = Yes (%)"                    "    46 (  5.6) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  3.24 (2.21)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.32 (4.11)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.83 (3.91)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.99 (3.26)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.69 (3.57)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   220 ( 26.7) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   220 ( 26.7) "                  
##   "MAT_Lang_0 = French (%)"                    "   220 ( 26.7) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   220 ( 26.7) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "138.16 (60.96)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   355 ( 44.3) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   232 ( 28.3) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  1187"                            
##   "Age_0 (mean (SD))"                          " 72.09 (5.35)"                     
##   "Sex_0 = M (%)"                              "  1187 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 27.19 (3.85)"                     
##   "Ethnicity_0 = White (%)"                    "  1156 ( 97.4) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "    73 (  6.1) "                   
##   "   Married"                                 "   937 ( 78.9) "                   
##   "   Separated"                               "    16 (  1.3) "                   
##   "   Single"                                  "    50 (  4.2) "                   
##   "   Widowed"                                 "   111 (  9.4) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "   870 ( 73.3) "                   
##   "   High School Diploma"                     "   125 ( 10.5) "                   
##   "   Less than High School Diploma"           "   100 (  8.4) "                   
##   "   Some College"                            "    92 (  7.8) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "    89 (  7.7) "                   
##   "   >$150k"                                  "    47 (  4.1) "                   
##   "   $100-150k"                               "    71 (  6.1) "                   
##   "   $20-50k"                                 "   547 ( 47.3) "                   
##   "   $50-100k"                                "   403 ( 34.8) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   152 ( 12.8) "                   
##   "   Assisted Living"                         "     9 (  0.8) "                   
##   "   House"                                   "  1009 ( 85.0) "                   
##   "   Other"                                   "    17 (  1.4) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   124 ( 10.8) "                   
##   "   Occasional drinker"                      "   118 ( 10.2) "                   
##   "   Regular drinker (at least once a month)" "   911 ( 79.0) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "    39 (  3.3) "                   
##   "   Former Smoker"                           "   865 ( 73.4) "                   
##   "   Never Smoked"                            "   267 ( 22.7) "                   
##   "   Occasional Smoker"                       "     7 (  0.6) "                   
##   "CESD_10_0 (mean (SD))"                      "  4.17 (3.69)"                     
##   "Anxiety_0 = Yes (%)"                        "    45 (  3.8) "                   
##   "Mood_Disord_0 = Yes (%)"                    "    87 (  7.3) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  3.32 (2.30)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.62 (3.68)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.91 (3.67)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.07 (3.24)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.04 (3.53)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "    88 (  7.4) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "    88 (  7.4) "                   
##   "MAT_Lang_0 = French (%)"                    "    88 (  7.4) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "    88 (  7.4) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "136.93 (63.79)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   541 ( 45.8) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   345 ( 29.1) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.834" ""     " 0.2"   
##   "Ethnicity_0 = White (%)"                    " 1.000" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.373" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           " 0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.044" ""     " 2.5"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.130" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.622" ""     " 2.9"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.616" ""     " 0.7"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.199" ""     " 0.5"   
##   "Anxiety_0 = Yes (%)"                        " 0.208" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.145" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.475" ""     " 4.6"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 0.083" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 0.609" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.574" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.032" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   " 0.694" ""     "18.3"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.516" ""     " 1.4"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.718" ""     " 0.3"

Females 65+

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "   827"                           
##   "Age_0 (mean (SD))"                          " 73.52 (5.56)"                    
##   "Sex_0 = F (%)"                              "   827 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.31 (5.19)"                    
##   "Ethnicity_0 = White (%)"                    "   816 ( 98.7) "                  
##   "Relationship_status_0 (%)"                  "   "                              
##   "   Divorced"                                "    98 ( 11.9) "                  
##   "   Married"                                 "   415 ( 50.2) "                  
##   "   Separated"                               "    12 (  1.5) "                  
##   "   Single"                                  "    46 (  5.6) "                  
##   "   Widowed"                                 "   256 ( 31.0) "                  
##   "Education4_0 (%)"                           "   "                              
##   "   College Degree or Higher"                "   444 ( 53.7) "                  
##   "   High School Diploma"                     "   167 ( 20.2) "                  
##   "   Less than High School Diploma"           "   148 ( 17.9) "                  
##   "   Some College"                            "    68 (  8.2) "                  
##   "Income_Level_0 (%)"                         "   "                              
##   "   <$20k"                                   "   261 ( 33.5) "                  
##   "   >$150k"                                  "     6 (  0.8) "                  
##   "   $100-150k"                               "    10 (  1.3) "                  
##   "   $20-50k"                                 "   399 ( 51.2) "                  
##   "   $50-100k"                                "   104 ( 13.3) "                  
##   "Living_status_0 (%)"                        "   "                              
##   "   Apartment/Condo/Townhome"                "   189 ( 22.9) "                  
##   "   Assisted Living"                         "    17 (  2.1) "                  
##   "   House"                                   "   608 ( 73.5) "                  
##   "   Other"                                   "    13 (  1.6) "                  
##   "Alcohol_0 (%)"                              "   "                              
##   "   Non-drinker"                             "   109 ( 14.1) "                  
##   "   Occasional drinker"                      "   186 ( 24.1) "                  
##   "   Regular drinker (at least once a month)" "   477 ( 61.8) "                  
##   "Smoking_Status_0 (%)"                       "   "                              
##   "   Daily Smoker"                            "    33 (  4.0) "                  
##   "   Former Smoker"                           "   459 ( 55.7) "                  
##   "   Never Smoked"                            "   324 ( 39.3) "                  
##   "   Occasional Smoker"                       "     8 (  1.0) "                  
##   "CESD_10_0 (mean (SD))"                      "  5.33 (4.22)"                    
##   "Anxiety_0 = Yes (%)"                        "    54 (  6.5) "                  
##   "Mood_Disord_0 = Yes (%)"                    "    97 ( 11.7) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  4.16 (2.60)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.92 (3.97)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 11.36 (3.91)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.96 (3.36)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.94 (3.71)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   218 ( 26.4) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   218 ( 26.4) "                  
##   "MAT_Lang_0 = French (%)"                    "   218 ( 26.4) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   218 ( 26.4) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "119.62 (54.01)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   383 ( 47.0) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   262 ( 31.7) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  1317"                            
##   "Age_0 (mean (SD))"                          " 72.42 (5.62)"                     
##   "Sex_0 = F (%)"                              "  1317 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 27.00 (5.16)"                     
##   "Ethnicity_0 = White (%)"                    "  1293 ( 98.2) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   193 ( 14.7) "                   
##   "   Married"                                 "   660 ( 50.2) "                   
##   "   Separated"                               "    19 (  1.4) "                   
##   "   Single"                                  "    79 (  6.0) "                   
##   "   Widowed"                                 "   364 ( 27.7) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "   975 ( 74.0) "                   
##   "   High School Diploma"                     "   157 ( 11.9) "                   
##   "   Less than High School Diploma"           "   106 (  8.0) "                   
##   "   Some College"                            "    79 (  6.0) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   315 ( 25.7) "                   
##   "   >$150k"                                  "     9 (  0.7) "                   
##   "   $100-150k"                               "    19 (  1.5) "                   
##   "   $20-50k"                                 "   667 ( 54.4) "                   
##   "   $50-100k"                                "   216 ( 17.6) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   272 ( 20.7) "                   
##   "   Assisted Living"                         "    23 (  1.7) "                   
##   "   House"                                   "  1006 ( 76.4) "                   
##   "   Other"                                   "    16 (  1.2) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   163 ( 13.2) "                   
##   "   Occasional drinker"                      "   269 ( 21.8) "                   
##   "   Regular drinker (at least once a month)" "   801 ( 65.0) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "    52 (  4.0) "                   
##   "   Former Smoker"                           "   763 ( 58.3) "                   
##   "   Never Smoked"                            "   484 ( 37.0) "                   
##   "   Occasional Smoker"                       "    10 (  0.8) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.13 (4.43)"                     
##   "Anxiety_0 = Yes (%)"                        "    90 (  6.8) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   156 ( 11.9) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  3.99 (2.59)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 11.18 (3.99)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 11.45 (3.84)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 11.20 (3.18)"                     
##   "MAT_Normed_0 (mean (SD))"                   "  9.97 (3.60)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   104 (  7.9) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "   104 (  7.9) "                   
##   "MAT_Lang_0 = French (%)"                    "   104 (  7.9) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "   104 (  7.9) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "115.93 (56.88)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   549 ( 41.9) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   435 ( 33.1) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = F (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.184" ""     " 0.7"   
##   "Ethnicity_0 = White (%)"                    " 0.484" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.283" ""     " 0.1"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.002" ""     " 6.4"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.487" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.347" ""     " 6.5"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.663" ""     " 0.5"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.294" ""     " 0.4"   
##   "Anxiety_0 = Yes (%)"                        " 0.855" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.985" ""     " 0.0"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.166" ""     " 5.5"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 0.140" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 0.589" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.103" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.837" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   " 0.193" ""     "23.2"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.025" ""     " 0.9"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.544" ""     " 0.1"

4) Linear mixed model set-up

Linear mixed model set-up

Tracking.data.short<-Tracking.Adjusted_Final[c(1,4:12,14,15,18:21,146,147,98,115,132,102,119,136,106,123,140,110,127,144,13,44,75,22,53,84,23,54,85)]

Tracking.data.short2<-rename(Tracking.data.short, c("Age"="Age_0","Sex"="Sex_0","Ethnicity"="Ethnicity_0","Relationshipstatus"="Relationship_status_0",
                             "Education"="Education4_0", "IncomeLevel"="Income_Level_0", "Livingstatus"="Living_status_0", 
                             "Alcohol"="Alcohol_0", "SmokingStatus"="Smoking_Status_0","Anxiety"="Anxiety_0","MoodDisord"="Mood_Disord_0",
                             "Chronicconditions"="Chronic_conditions_0", "BMI"="BMI_0","PASE_Sit_0"="PASE_Q1B_0","PASE_Sit_1"="PASE_Q1B_1","PASE_Sit_2"="PASE_Q1B_2"))

Tracking.data.short2$PASE_TOTALbaseline <- Tracking.data.short2$PASE_TOTAL_0

Tracking.data.short3<-Tracking.data.short2[c(1:18,40,19:39)]
Tracking.data.short4<-Tracking.data.short3[c(1:18,20,23,26,29,32,35,38,21:22,24:25,27:28,30:31,33:34,36:37,39:40)]
colnames(Tracking.data.short3) <- (gsub("_2",".3",colnames(Tracking.data.short3)))
colnames(Tracking.data.short3) <- (gsub("_1",".2",colnames(Tracking.data.short3)))
colnames(Tracking.data.short3) <- (gsub("_0",".1",colnames(Tracking.data.short3)))

colnames(Tracking.data.short4) <- (gsub("_2",".2",colnames(Tracking.data.short4)))
colnames(Tracking.data.short4) <- (gsub("_1",".1",colnames(Tracking.data.short4)))
colnames(Tracking.data.short4) <- (gsub("_0","baseline",colnames(Tracking.data.short4)))


Tracking.data_long <- reshape(as.data.frame(Tracking.data.short3),idvar="ID",varying=20:40,direction="long",sep=".") #reshape data into long format (3 timepoints)
Tracking.data_long_2 <- reshape(as.data.frame(Tracking.data.short4),idvar="ID",varying=26:39,direction="long",sep=".") #reshape data into long format (3 timepoints)

Indexed time as a categorical factor

#Treat time as a fixed effect
Tracking.data_long$timefactor<-as.factor(Tracking.data_long$time)
Tracking.data_long_2$timefactor<-as.factor(Tracking.data_long_2$time)

5) Main Effects Model

All models use normalized cognitive scores. Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

Contrast statements

#Contrast 1: Group differences from FU1 to FU2 (for significant effects)
c1=matrix(c(0,1,0,-1))
c2=matrix(c(1,0,-1,0))
c1st=c1 - c2

#Contrast 2: 65+ males and group differences from FU1 to FU2 (for significant effects)
c3=matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,1,0,-1,0))
c4=matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,-1))
c2nd=c3 - c4

#Contrast 3: 65+ females and group differences from FU1 to FU2 (for significant effects)
c5=matrix(c(0,0,0,0,1,0,-1,0,0,0,0,0,0,0,0,0))
c6=matrix(c(0,0,0,0,0,1,0,-1,0,0,0,0,0,0,0,0))
c3rd=c5 - c6

#Contrast 3: 45-64 females and group differences from FU1 to FU2 (for significant effects)
c7=matrix(c(1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0))
c8=matrix(c(0,1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0))
c4th=c7 - c8

emm_options(opt.digits = FALSE)
emm_options(pbkrtest.limit = 50000)
set.seed(1)

5.1) RVLT Immediate Recall

5.1.1) Model

modelRVLT_imm_adj10<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + RVLT_Immediate_Normedbaseline + (1 |      ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 105600.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6632 -0.5628 -0.0352  0.5307  3.8380 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.154    2.038   
##  Residual             7.456    2.731   
## Number of obs: 20202, groups:  ID, 10396
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             8.270e+00  3.554e-01
## timefactor2                                             3.271e-01  5.737e-02
## PandemicFU2 data collected before COVID-19              1.812e-01  6.889e-02
## Age                                                    -2.367e-02  3.395e-03
## SexM                                                   -5.478e-01  6.209e-02
## EducationHigh School Diploma                            2.302e-01  8.733e-02
## EducationLess than High School Diploma                  5.084e-01  1.207e-01
## EducationSome College                                   1.737e-01  1.090e-01
## EthnicityWhite                                          6.505e-01  1.713e-01
## IncomeLevel>$150k                                       7.030e-01  1.644e-01
## IncomeLevel$100-150k                                    5.749e-01  1.308e-01
## IncomeLevel$20-50k                                      2.324e-01  8.533e-02
## IncomeLevel$50-100k                                     5.311e-01  9.238e-02
## BMI                                                    -1.667e-02  5.664e-03
## CESD.10baseline                                        -2.056e-02  6.930e-03
## SmokingStatusFormer Smoker                              7.653e-02  1.169e-01
## SmokingStatusNever Smoked                               2.201e-01  1.220e-01
## SmokingStatusOccasional Smoker                         -1.183e-01  2.385e-01
## RelationshipstatusMarried                               2.324e-01  1.028e-01
## RelationshipstatusSeparated                             1.104e-01  2.009e-01
## RelationshipstatusSingle                                5.907e-02  1.391e-01
## RelationshipstatusWidowed                              -5.266e-02  1.381e-01
## LivingstatusAssisted Living                            -1.093e+00  4.033e-01
## LivingstatusHouse                                       7.423e-02  9.177e-02
## LivingstatusOther                                       3.873e-02  3.145e-01
## AnxietyYes                                             -8.563e-03  1.209e-01
## MoodDisordYes                                          -1.435e-01  8.828e-02
## Chronicconditions                                      -2.969e-02  1.411e-02
## RVLT_Immediate_Normedbaseline                           3.479e-01  7.409e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -9.844e-02  7.759e-02
##                                                                df t value
## (Intercept)                                             1.044e+04  23.267
## timefactor2                                             1.019e+04   5.702
## PandemicFU2 data collected before COVID-19              1.783e+04   2.630
## Age                                                     1.033e+04  -6.972
## SexM                                                    1.031e+04  -8.822
## EducationHigh School Diploma                            1.033e+04   2.636
## EducationLess than High School Diploma                  1.045e+04   4.211
## EducationSome College                                   1.022e+04   1.593
## EthnicityWhite                                          1.026e+04   3.796
## IncomeLevel>$150k                                       1.031e+04   4.275
## IncomeLevel$100-150k                                    1.027e+04   4.396
## IncomeLevel$20-50k                                      1.032e+04   2.724
## IncomeLevel$50-100k                                     1.031e+04   5.749
## BMI                                                     1.026e+04  -2.943
## CESD.10baseline                                         1.032e+04  -2.967
## SmokingStatusFormer Smoker                              1.038e+04   0.655
## SmokingStatusNever Smoked                               1.037e+04   1.804
## SmokingStatusOccasional Smoker                          1.026e+04  -0.496
## RelationshipstatusMarried                               1.034e+04   2.259
## RelationshipstatusSeparated                             1.040e+04   0.549
## RelationshipstatusSingle                                1.033e+04   0.425
## RelationshipstatusWidowed                               1.034e+04  -0.381
## LivingstatusAssisted Living                             1.031e+04  -2.711
## LivingstatusHouse                                       1.032e+04   0.809
## LivingstatusOther                                       1.021e+04   0.123
## AnxietyYes                                              1.031e+04  -0.071
## MoodDisordYes                                           1.031e+04  -1.625
## Chronicconditions                                       1.031e+04  -2.105
## RVLT_Immediate_Normedbaseline                           1.033e+04  46.952
## timefactor2:PandemicFU2 data collected before COVID-19  1.013e+04  -1.269
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.22e-08 ***
## PandemicFU2 data collected before COVID-19             0.008537 ** 
## Age                                                    3.33e-12 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.008404 ** 
## EducationLess than High School Diploma                 2.56e-05 ***
## EducationSome College                                  0.111152    
## EthnicityWhite                                         0.000148 ***
## IncomeLevel>$150k                                      1.93e-05 ***
## IncomeLevel$100-150k                                   1.11e-05 ***
## IncomeLevel$20-50k                                     0.006470 ** 
## IncomeLevel$50-100k                                    9.22e-09 ***
## BMI                                                    0.003253 ** 
## CESD.10baseline                                        0.003019 ** 
## SmokingStatusFormer Smoker                             0.512786    
## SmokingStatusNever Smoked                              0.071284 .  
## SmokingStatusOccasional Smoker                         0.619858    
## RelationshipstatusMarried                              0.023873 *  
## RelationshipstatusSeparated                            0.582676    
## RelationshipstatusSingle                               0.671102    
## RelationshipstatusWidowed                              0.702919    
## LivingstatusAssisted Living                            0.006726 ** 
## LivingstatusHouse                                      0.418608    
## LivingstatusOther                                      0.901994    
## AnxietyYes                                             0.943542    
## MoodDisordYes                                          0.104185    
## Chronicconditions                                      0.035338 *  
## RVLT_Immediate_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.204590    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_imm_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF DenDF   F value    Pr(>F)
## timefactor                      382.6   382.6     1 10128   51.3079 8.445e-13
## Pandemic                         39.6    39.6     1 10316    5.3115 0.0212044
## Age                             362.4   362.4     1 10332   48.6029 3.326e-12
## Sex                             580.3   580.3     1 10307   77.8248 < 2.2e-16
## Education                       168.2    56.1     3 10336    7.5176 5.061e-05
## Ethnicity                       107.5   107.5     1 10257   14.4121 0.0001477
## IncomeLevel                     332.4    83.1     4 10293   11.1444 5.086e-09
## BMI                              64.6    64.6     1 10258    8.6640 0.0032529
## CESD.10baseline                  65.6    65.6     1 10324    8.8002 0.0030190
## SmokingStatus                    57.5    19.2     3 10300    2.5720 0.0523205
## Relationshipstatus               85.1    21.3     4 10351    2.8516 0.0224059
## Livingstatus                     67.0    22.3     3 10277    2.9955 0.0295176
## Anxiety                           0.0     0.0     1 10315    0.0050 0.9435424
## MoodDisord                       19.7    19.7     1 10307    2.6407 0.1041850
## Chronicconditions                33.0    33.0     1 10314    4.4299 0.0353380
## RVLT_Immediate_Normedbaseline 16437.2 16437.2     1 10327 2204.4451 < 2.2e-16
## timefactor:Pandemic              12.0    12.0     1 10128    1.6095 0.2045900
##                                  
## timefactor                    ***
## Pandemic                      *  
## Age                           ***
## Sex                           ***
## Education                     ***
## Ethnicity                     ***
## IncomeLevel                   ***
## BMI                           ** 
## CESD.10baseline               ** 
## SmokingStatus                 .  
## Relationshipstatus            *  
## Livingstatus                  *  
## Anxiety                          
## MoodDisord                       
## Chronicconditions             *  
## RVLT_Immediate_Normedbaseline ***
## timefactor:Pandemic              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.29666 0.1827283 10768.87  9.938482
##  FU2 data collected before COVID-19 10.47788 0.1831489 10701.03 10.118874
##  upper.CL
##  10.65484
##  10.83689
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.62379 0.1829312 10800.00 10.265211
##  FU2 data collected before COVID-19 10.70656 0.1830793 10690.38 10.347696
##  upper.CL
##  10.98237
##  11.06544
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##  -0.18121676 0.06889484 17828.92  -2.630  0.0085
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##  -0.08277509 0.06945803 17963.18  -1.192  0.2334
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL    upper.CL
##  -0.18121676 0.06889484 17828.92 -0.3162573 -0.04617619
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL    upper.CL
##  -0.08277509 0.06945803 17963.18 -0.2189195  0.05336933
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

5.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj10 <- summary(lsmeans(modelRVLT_imm_adj10, ~timefactor|Pandemic))
RVLTimmediate_lsmeans_adj10$Time<-NA
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

5.1.4) Planned contrasts

Test whether differences between cohorts at FU1 and FU2 are significant

lsmeans.RVLTImm10 <- lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)
contrast(lsmeans.RVLTImm10,list(c1st),by=NULL)
##  contrast                                      estimate         SE       df
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.09844167 0.07759502 10134.68
##  t.ratio p.value
##    1.269  0.2046
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger

5.2) RVLT Delayed Recall

5.2.1) Model

modelRVLT_del_adj10<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + RVLT_Delayed_Normedbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 103623.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9202 -0.5513 -0.0366  0.5075  4.1570 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.099    2.025   
##  Residual             6.955    2.637   
## Number of obs: 20030, groups:  ID, 10379
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.308e+00  3.505e-01
## timefactor2                                             5.842e-01  5.580e-02
## PandemicFU2 data collected before COVID-19              2.840e-01  6.748e-02
## Age                                                    -1.378e-02  3.345e-03
## SexM                                                   -4.890e-01  6.101e-02
## EducationHigh School Diploma                            3.149e-01  8.587e-02
## EducationLess than High School Diploma                  3.300e-01  1.192e-01
## EducationSome College                                   2.152e-01  1.071e-01
## EthnicityWhite                                          7.508e-01  1.691e-01
## IncomeLevel>$150k                                       5.890e-01  1.615e-01
## IncomeLevel$100-150k                                    4.381e-01  1.286e-01
## IncomeLevel$20-50k                                      2.284e-01  8.397e-02
## IncomeLevel$50-100k                                     5.421e-01  9.082e-02
## BMI                                                    -1.686e-02  5.563e-03
## CESD.10baseline                                        -2.056e-02  6.818e-03
## SmokingStatusFormer Smoker                              6.218e-02  1.149e-01
## SmokingStatusNever Smoked                               2.801e-01  1.199e-01
## SmokingStatusOccasional Smoker                          1.684e-02  2.345e-01
## RelationshipstatusMarried                               5.088e-03  1.012e-01
## RelationshipstatusSeparated                            -1.370e-01  1.976e-01
## RelationshipstatusSingle                               -7.281e-02  1.367e-01
## RelationshipstatusWidowed                              -2.265e-01  1.358e-01
## LivingstatusAssisted Living                            -1.246e+00  3.977e-01
## LivingstatusHouse                                       1.297e-01  9.025e-02
## LivingstatusOther                                      -4.025e-02  3.104e-01
## AnxietyYes                                              8.321e-02  1.188e-01
## MoodDisordYes                                          -1.630e-01  8.670e-02
## Chronicconditions                                      -4.247e-02  1.387e-02
## RVLT_Delayed_Normedbaseline                             3.927e-01  7.464e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -3.051e-01  7.539e-02
##                                                                df t value
## (Intercept)                                             1.047e+04  20.854
## timefactor2                                             1.013e+04  10.468
## PandemicFU2 data collected before COVID-19              1.762e+04   4.209
## Age                                                     1.034e+04  -4.120
## SexM                                                    1.029e+04  -8.015
## EducationHigh School Diploma                            1.032e+04   3.667
## EducationLess than High School Diploma                  1.050e+04   2.769
## EducationSome College                                   1.021e+04   2.009
## EthnicityWhite                                          1.034e+04   4.439
## IncomeLevel>$150k                                       1.029e+04   3.648
## IncomeLevel$100-150k                                    1.027e+04   3.406
## IncomeLevel$20-50k                                      1.033e+04   2.721
## IncomeLevel$50-100k                                     1.030e+04   5.968
## BMI                                                     1.022e+04  -3.031
## CESD.10baseline                                         1.034e+04  -3.016
## SmokingStatusFormer Smoker                              1.035e+04   0.541
## SmokingStatusNever Smoked                               1.034e+04   2.337
## SmokingStatusOccasional Smoker                          1.027e+04   0.072
## RelationshipstatusMarried                               1.030e+04   0.050
## RelationshipstatusSeparated                             1.041e+04  -0.694
## RelationshipstatusSingle                                1.027e+04  -0.533
## RelationshipstatusWidowed                               1.031e+04  -1.668
## LivingstatusAssisted Living                             1.041e+04  -3.133
## LivingstatusHouse                                       1.032e+04   1.437
## LivingstatusOther                                       1.033e+04  -0.130
## AnxietyYes                                              1.029e+04   0.700
## MoodDisordYes                                           1.028e+04  -1.880
## Chronicconditions                                       1.030e+04  -3.062
## RVLT_Delayed_Normedbaseline                             1.029e+04  52.613
## timefactor2:PandemicFU2 data collected before COVID-19  1.006e+04  -4.047
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             2.58e-05 ***
## Age                                                    3.82e-05 ***
## SexM                                                   1.22e-15 ***
## EducationHigh School Diploma                           0.000247 ***
## EducationLess than High School Diploma                 0.005628 ** 
## EducationSome College                                  0.044599 *  
## EthnicityWhite                                         9.12e-06 ***
## IncomeLevel>$150k                                      0.000266 ***
## IncomeLevel$100-150k                                   0.000662 ***
## IncomeLevel$20-50k                                     0.006528 ** 
## IncomeLevel$50-100k                                    2.48e-09 ***
## BMI                                                    0.002443 ** 
## CESD.10baseline                                        0.002564 ** 
## SmokingStatusFormer Smoker                             0.588373    
## SmokingStatusNever Smoked                              0.019480 *  
## SmokingStatusOccasional Smoker                         0.942767    
## RelationshipstatusMarried                              0.959889    
## RelationshipstatusSeparated                            0.487961    
## RelationshipstatusSingle                               0.594311    
## RelationshipstatusWidowed                              0.095282 .  
## LivingstatusAssisted Living                            0.001738 ** 
## LivingstatusHouse                                      0.150622    
## LivingstatusOther                                      0.896827    
## AnxietyYes                                             0.483834    
## MoodDisordYes                                          0.060177 .  
## Chronicconditions                                      0.002203 ** 
## RVLT_Delayed_Normedbaseline                             < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 5.23e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_del_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF DenDF   F value    Pr(>F)    
## timefactor                    911.8   911.8     1 10061  131.1009 < 2.2e-16 ***
## Pandemic                       37.9    37.9     1 10308    5.4531 0.0195524 *  
## Age                           118.1   118.1     1 10342   16.9759 3.815e-05 ***
## Sex                           446.7   446.7     1 10295   64.2369 1.223e-15 ***
## Education                     140.2    46.7     3 10344    6.7190 0.0001589 ***
## Ethnicity                     137.1   137.1     1 10343   19.7076 9.118e-06 ***
## IncomeLevel                   295.0    73.8     4 10284   10.6061 1.417e-08 ***
## BMI                            63.9    63.9     1 10222    9.1877 0.0024425 ** 
## CESD.10baseline                63.3    63.3     1 10340    9.0991 0.0025636 ** 
## SmokingStatus                 100.6    33.5     3 10295    4.8210 0.0023486 ** 
## Relationshipstatus             37.4     9.3     4 10334    1.3435 0.2511201    
## Livingstatus                   97.0    32.3     3 10358    4.6502 0.0029853 ** 
## Anxiety                         3.4     3.4     1 10289    0.4902 0.4838338    
## MoodDisord                     24.6    24.6     1 10279    3.5333 0.0601766 .  
## Chronicconditions              65.2    65.2     1 10299    9.3769 0.0022030 ** 
## RVLT_Delayed_Normedbaseline 19251.5 19251.5     1 10292 2768.1443 < 2.2e-16 ***
## timefactor:Pandemic           113.9   113.9     1 10061   16.3755 5.234e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.37762 0.1800165 10808.72 10.02476
##  FU2 data collected before COVID-19 10.66160 0.1804340 10738.92 10.30792
##  upper.CL
##  10.73049
##  11.01528
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.96180 0.1802059 10838.82 10.60856
##  FU2 data collected before COVID-19 10.94068 0.1803483 10725.46 10.58717
##  upper.CL
##  11.31503
##  11.29420
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##  -0.28397783 0.06747662 17616.67  -4.209  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##   0.02111468 0.06800440 17749.18   0.310  0.7562
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL  upper.CL
##  -0.28397783 0.06747662 17616.67 -0.4162387 -0.151717
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL  upper.CL
##   0.02111468 0.06800440 17749.18 -0.1121806  0.154410
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

5.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj10 <- summary(lsmeans(modelRVLT_del_adj10, ~timefactor|Pandemic))
RVLTdelayed_lsmeans_adj10$Time<-NA
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

5.2.4) Planned contrasts

Test whether differences between cohorts at FU1 and FU2 are significant

lsmeans.RVLTDel10 <- lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)
contrast(lsmeans.RVLTDel10,list(c1st),by=NULL)
##  contrast                                     estimate         SE      df
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.3050925 0.07539398 10051.4
##  t.ratio p.value
##    4.047  0.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger

5.3) Mental Alteration Test

5.3.1) Model

modelMAT_adj10<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + MAT_Normedbaseline +
                      (1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + MAT_Normedbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 98080.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4255 -0.5244 -0.0823  0.3678  4.8694 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.322    1.524   
##  Residual             8.535    2.921   
## Number of obs: 18841, groups:  ID, 10262
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.772e+00  3.383e-01
## timefactor2                                            -1.408e+00  6.399e-02
## PandemicFU2 data collected before COVID-19             -5.110e-01  6.897e-02
## Age                                                    -1.182e-02  3.216e-03
## SexM                                                   -1.185e+00  5.842e-02
## EducationHigh School Diploma                           -4.450e-03  8.238e-02
## EducationLess than High School Diploma                 -1.563e-01  1.158e-01
## EducationSome College                                  -1.551e-01  1.021e-01
## EthnicityWhite                                          9.209e-01  1.630e-01
## IncomeLevel>$150k                                       1.290e-01  1.546e-01
## IncomeLevel$100-150k                                    1.601e-01  1.233e-01
## IncomeLevel$20-50k                                      1.050e-01  8.082e-02
## IncomeLevel$50-100k                                     1.458e-01  8.740e-02
## BMI                                                    -1.546e-02  5.326e-03
## CESD.10baseline                                        -1.402e-02  6.537e-03
## SmokingStatusFormer Smoker                              5.973e-02  1.099e-01
## SmokingStatusNever Smoked                              -6.673e-02  1.147e-01
## SmokingStatusOccasional Smoker                          1.143e-01  2.245e-01
## RelationshipstatusMarried                               3.628e-02  9.715e-02
## RelationshipstatusSeparated                            -7.207e-02  1.887e-01
## RelationshipstatusSingle                                5.128e-01  1.312e-01
## RelationshipstatusWidowed                              -4.490e-02  1.311e-01
## LivingstatusAssisted Living                            -2.329e-01  3.840e-01
## LivingstatusHouse                                      -2.095e-01  8.678e-02
## LivingstatusOther                                      -7.092e-01  2.976e-01
## AnxietyYes                                              1.399e-01  1.138e-01
## MoodDisordYes                                           1.001e-02  8.293e-02
## Chronicconditions                                      -4.277e-02  1.336e-02
## MAT_Normedbaseline                                      4.411e-01  7.721e-03
## timefactor2:PandemicFU2 data collected before COVID-19  5.356e-01  8.637e-02
##                                                                df t value
## (Intercept)                                             1.017e+04  22.974
## timefactor2                                             9.732e+03 -21.999
## PandemicFU2 data collected before COVID-19              1.813e+04  -7.409
## Age                                                     1.014e+04  -3.674
## SexM                                                    9.967e+03 -20.280
## EducationHigh School Diploma                            9.974e+03  -0.054
## EducationLess than High School Diploma                  1.025e+04  -1.349
## EducationSome College                                   9.837e+03  -1.519
## EthnicityWhite                                          1.014e+04   5.650
## IncomeLevel>$150k                                       9.915e+03   0.835
## IncomeLevel$100-150k                                    9.935e+03   1.299
## IncomeLevel$20-50k                                      1.004e+04   1.299
## IncomeLevel$50-100k                                     9.974e+03   1.668
## BMI                                                     9.816e+03  -2.902
## CESD.10baseline                                         9.939e+03  -2.144
## SmokingStatusFormer Smoker                              9.971e+03   0.544
## SmokingStatusNever Smoked                               9.964e+03  -0.582
## SmokingStatusOccasional Smoker                          9.855e+03   0.509
## RelationshipstatusMarried                               1.005e+04   0.373
## RelationshipstatusSeparated                             1.001e+04  -0.382
## RelationshipstatusSingle                                1.000e+04   3.910
## RelationshipstatusWidowed                               1.013e+04  -0.343
## LivingstatusAssisted Living                             1.016e+04  -0.607
## LivingstatusHouse                                       1.005e+04  -2.415
## LivingstatusOther                                       9.856e+03  -2.383
## AnxietyYes                                              9.972e+03   1.229
## MoodDisordYes                                           9.905e+03   0.121
## Chronicconditions                                       1.008e+04  -3.201
## MAT_Normedbaseline                                      1.004e+04  57.130
## timefactor2:PandemicFU2 data collected before COVID-19  9.653e+03   6.201
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             1.32e-13 ***
## Age                                                     0.00024 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                            0.95692    
## EducationLess than High School Diploma                  0.17734    
## EducationSome College                                   0.12886    
## EthnicityWhite                                         1.65e-08 ***
## IncomeLevel>$150k                                       0.40393    
## IncomeLevel$100-150k                                    0.19402    
## IncomeLevel$20-50k                                      0.19383    
## IncomeLevel$50-100k                                     0.09538 .  
## BMI                                                     0.00371 ** 
## CESD.10baseline                                         0.03204 *  
## SmokingStatusFormer Smoker                              0.58678    
## SmokingStatusNever Smoked                               0.56073    
## SmokingStatusOccasional Smoker                          0.61064    
## RelationshipstatusMarried                               0.70883    
## RelationshipstatusSeparated                             0.70247    
## RelationshipstatusSingle                               9.30e-05 ***
## RelationshipstatusWidowed                               0.73194    
## LivingstatusAssisted Living                             0.54416    
## LivingstatusHouse                                       0.01576 *  
## LivingstatusOther                                       0.01720 *  
## AnxietyYes                                              0.21912    
## MoodDisordYes                                           0.90398    
## Chronicconditions                                       0.00138 ** 
## MAT_Normedbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 5.86e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelMAT_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
##                      Sum Sq Mean Sq NumDF   DenDF   F value    Pr(>F)    
## timefactor           5944.5  5944.5     1  9651.9  696.5100 < 2.2e-16 ***
## Pandemic              173.1   173.1     1 10008.1   20.2828 6.756e-06 ***
## Age                   115.2   115.2     1 10143.2   13.4993 0.0002399 ***
## Sex                  3510.3  3510.3     1  9967.0  411.2965 < 2.2e-16 ***
## Education              32.6    10.9     3 10020.4    1.2743 0.2813077    
## Ethnicity             272.4   272.4     1 10144.8   31.9206 1.649e-08 ***
## IncomeLevel            25.6     6.4     4  9942.1    0.7487 0.5587154    
## BMI                    71.9    71.9     1  9815.8    8.4241 0.0037111 ** 
## CESD.10baseline        39.2    39.2     1  9939.1    4.5976 0.0320405 *  
## SmokingStatus          42.1    14.0     3  9915.8    1.6430 0.1771420    
## Relationshipstatus    209.5    52.4     4 10036.3    6.1367 6.285e-05 ***
## Livingstatus           79.6    26.5     3 10020.5    3.1078 0.0253301 *  
## Anxiety                12.9    12.9     1  9972.2    1.5103 0.2191165    
## MoodDisord              0.1     0.1     1  9905.2    0.0146 0.9039759    
## Chronicconditions      87.4    87.4     1 10079.5   10.2442 0.0013754 ** 
## MAT_Normedbaseline  27855.4 27855.4     1 10042.1 3263.8035 < 2.2e-16 ***
## timefactor:Pandemic   328.1   328.1     1  9652.6   38.4475 5.855e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2) Estimated marginal means

lsmeans(modelMAT_adj10, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.665980 0.1741807 10709.43 10.324554
##  FU2 data collected before COVID-19 10.154966 0.1742890 10591.05  9.813327
##   upper.CL
##  11.007407
##  10.496605
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.258314 0.1740161 10687.80  8.917210
##  FU2 data collected before COVID-19  9.282878 0.1741834 10577.03  8.941446
##   upper.CL
##   9.599418
##   9.624310
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##   0.5110145 0.06896917 18128.64   7.409  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.0245641 0.06933355 18159.72  -0.354  0.7231
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL  upper.CL
##   0.5110145 0.06896917 18128.64  0.3758284 0.6462006
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL  upper.CL
##  -0.0245641 0.06933355 18159.72 -0.1604644 0.1113362
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

5.3.3) Graph of estimated marginal means

MAT_lsmeans_adj10 <- summary(lsmeans(modelMAT_adj10, ~Pandemic|timefactor))
MAT_lsmeans_adj10$Time<-NA
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

5.3.4) Planned contrasts

Test whether differences between cohorts at FU1 and FU2 are significant

lsmeans.MAT10 <- lsmeans(modelMAT_adj10, ~Pandemic|timefactor)
contrast(lsmeans.MAT10,list(c1st),by=NULL)
##  contrast                                      estimate        SE      df
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5355786 0.0863762 9673.68
##  t.ratio p.value
##   -6.201  <.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger

5.4) Animal Fluency

5.4.1) Model

modelAnimals_adj10<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + Animal_Fluency_Normedbaseline +
                           (1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + Animal_Fluency_Normedbaseline + (1 |      ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 94871.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8664 -0.5429 -0.0218  0.5261  4.5657 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.996    1.731   
##  Residual             3.870    1.967   
## Number of obs: 20333, groups:  ID, 10403
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             5.746e+00  2.835e-01
## timefactor2                                             5.077e-02  4.127e-02
## PandemicFU2 data collected before COVID-19              1.944e-01  5.280e-02
## Age                                                    -2.582e-02  2.665e-03
## SexM                                                   -9.745e-02  4.884e-02
## EducationHigh School Diploma                            2.223e-01  6.891e-02
## EducationLess than High School Diploma                  7.748e-02  9.497e-02
## EducationSome College                                   2.309e-01  8.596e-02
## EthnicityWhite                                          6.059e-01  1.353e-01
## IncomeLevel>$150k                                       1.162e-01  1.295e-01
## IncomeLevel$100-150k                                    1.947e-01  1.032e-01
## IncomeLevel$20-50k                                     -1.037e-02  6.726e-02
## IncomeLevel$50-100k                                     1.725e-01  7.282e-02
## BMI                                                    -5.115e-03  4.466e-03
## CESD.10baseline                                        -1.453e-02  5.471e-03
## SmokingStatusFormer Smoker                              1.002e-01  9.210e-02
## SmokingStatusNever Smoked                               8.221e-02  9.613e-02
## SmokingStatusOccasional Smoker                         -6.541e-02  1.887e-01
## RelationshipstatusMarried                               7.248e-02  8.114e-02
## RelationshipstatusSeparated                             2.660e-01  1.583e-01
## RelationshipstatusSingle                                5.200e-02  1.097e-01
## RelationshipstatusWidowed                               2.219e-02  1.088e-01
## LivingstatusAssisted Living                             4.125e-02  3.177e-01
## LivingstatusHouse                                       2.059e-01  7.241e-02
## LivingstatusOther                                      -4.868e-02  2.478e-01
## AnxietyYes                                              3.443e-02  9.542e-02
## MoodDisordYes                                           9.391e-02  6.966e-02
## Chronicconditions                                      -2.005e-02  1.111e-02
## Animal_Fluency_Normedbaseline                           5.566e-01  6.452e-03
## timefactor2:PandemicFU2 data collected before COVID-19  9.056e-03  5.573e-02
##                                                                df t value
## (Intercept)                                             1.043e+04  20.267
## timefactor2                                             1.022e+04   1.230
## PandemicFU2 data collected before COVID-19              1.699e+04   3.682
## Age                                                     1.033e+04  -9.688
## SexM                                                    1.032e+04  -1.995
## EducationHigh School Diploma                            1.035e+04   3.226
## EducationLess than High School Diploma                  1.043e+04   0.816
## EducationSome College                                   1.027e+04   2.686
## EthnicityWhite                                          1.029e+04   4.476
## IncomeLevel>$150k                                       1.032e+04   0.897
## IncomeLevel$100-150k                                    1.030e+04   1.887
## IncomeLevel$20-50k                                      1.033e+04  -0.154
## IncomeLevel$50-100k                                     1.032e+04   2.368
## BMI                                                     1.028e+04  -1.145
## CESD.10baseline                                         1.036e+04  -2.656
## SmokingStatusFormer Smoker                              1.038e+04   1.088
## SmokingStatusNever Smoked                               1.037e+04   0.855
## SmokingStatusOccasional Smoker                          1.029e+04  -0.347
## RelationshipstatusMarried                               1.036e+04   0.893
## RelationshipstatusSeparated                             1.038e+04   1.681
## RelationshipstatusSingle                                1.035e+04   0.474
## RelationshipstatusWidowed                               1.033e+04   0.204
## LivingstatusAssisted Living                             1.029e+04   0.130
## LivingstatusHouse                                       1.034e+04   2.844
## LivingstatusOther                                       1.019e+04  -0.196
## AnxietyYes                                              1.034e+04   0.361
## MoodDisordYes                                           1.034e+04   1.348
## Chronicconditions                                       1.030e+04  -1.805
## Animal_Fluency_Normedbaseline                           1.031e+04  86.267
## timefactor2:PandemicFU2 data collected before COVID-19  1.015e+04   0.162
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.218714    
## PandemicFU2 data collected before COVID-19             0.000232 ***
## Age                                                     < 2e-16 ***
## SexM                                                   0.046025 *  
## EducationHigh School Diploma                           0.001259 ** 
## EducationLess than High School Diploma                 0.414623    
## EducationSome College                                  0.007245 ** 
## EthnicityWhite                                         7.67e-06 ***
## IncomeLevel>$150k                                      0.369861    
## IncomeLevel$100-150k                                   0.059231 .  
## IncomeLevel$20-50k                                     0.877467    
## IncomeLevel$50-100k                                    0.017881 *  
## BMI                                                    0.252095    
## CESD.10baseline                                        0.007920 ** 
## SmokingStatusFormer Smoker                             0.276730    
## SmokingStatusNever Smoked                              0.392461    
## SmokingStatusOccasional Smoker                         0.728827    
## RelationshipstatusMarried                              0.371739    
## RelationshipstatusSeparated                            0.092828 .  
## RelationshipstatusSingle                               0.635404    
## RelationshipstatusWidowed                              0.838382    
## LivingstatusAssisted Living                            0.896701    
## LivingstatusHouse                                      0.004464 ** 
## LivingstatusOther                                      0.844262    
## AnxietyYes                                             0.718279    
## MoodDisordYes                                          0.177633    
## Chronicconditions                                      0.071161 .  
## Animal_Fluency_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.870926    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelAnimals_adj10)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF DenDF   F value    Pr(>F)
## timefactor                       15.2    15.2     1 10153    3.9375  0.047247
## Pandemic                         75.3    75.3     1 10337   19.4654 1.035e-05
## Age                             363.2   363.2     1 10332   93.8618 < 2.2e-16
## Sex                              15.4    15.4     1 10316    3.9817  0.046025
## Education                        60.2    20.1     3 10349    5.1889  0.001398
## Ethnicity                        77.5    77.5     1 10293   20.0379 7.674e-06
## IncomeLevel                      53.8    13.5     4 10317    3.4779  0.007609
## BMI                               5.1     5.1     1 10277    1.3118  0.252095
## CESD.10baseline                  27.3    27.3     1 10355    7.0542  0.007920
## SmokingStatus                     7.8     2.6     3 10321    0.6677  0.571781
## Relationshipstatus               12.1     3.0     4 10348    0.7840  0.535339
## Livingstatus                     34.9    11.6     3 10269    3.0091  0.028977
## Anxiety                           0.5     0.5     1 10338    0.1302  0.718279
## MoodDisord                        7.0     7.0     1 10340    1.8176  0.177633
## Chronicconditions                12.6    12.6     1 10303    3.2567  0.071161
## Animal_Fluency_Normedbaseline 28800.0 28800.0     1 10311 7442.0627 < 2.2e-16
## timefactor:Pandemic               0.1     0.1     1 10153    0.0264  0.870926
##                                  
## timefactor                    *  
## Pandemic                      ***
## Age                           ***
## Sex                           *  
## Education                     ** 
## Ethnicity                     ***
## IncomeLevel                   ** 
## BMI                              
## CESD.10baseline               ** 
## SmokingStatus                    
## Relationshipstatus               
## Livingstatus                  *  
## Anxiety                          
## MoodDisord                       
## Chronicconditions             .  
## Animal_Fluency_Normedbaseline ***
## timefactor:Pandemic              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.4.2) Estimated marginal means

lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.84939 0.1438453 10712.42 10.56743
##  FU2 data collected before COVID-19 11.04381 0.1441413 10644.83 10.76127
##  upper.CL
##  11.13136
##  11.32636
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.90016 0.1439582 10735.89 10.61797
##  FU2 data collected before COVID-19 11.10364 0.1441642 10649.60 10.82105
##  upper.CL
##  11.18234
##  11.38623
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.1944217 0.05280075 17004.38  -3.682  0.0002
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.2034773 0.05321545 17176.59  -3.824  0.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.1944217 0.05280075 17004.38 -0.2979167 -0.09092677
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.2034773 0.05321545 17176.59 -0.3077850 -0.09916961
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

5.4.3) Graph of estimated marginal means

Animals_lsmeans_adj10 <- summary(lsmeans(modelAnimals_adj10, ~timefactor|Pandemic))
Animals_lsmeans_adj10$Time<-NA
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

5.4.4) Planned contrasts

Test whether differences between cohorts at FU1 and FU2 are significant

lsmeans.Animals10 <- lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)
contrast(lsmeans.Animals10,list(c1st),by=NULL)
##  contrast                                        estimate         SE       df
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.009055605 0.05573164 10171.64
##  t.ratio p.value
##   -0.162  0.8709
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger

6) Age and Sex Interaction Model

All models use normalized cognitive scores. Each model is adjusted for education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

Age and Sex grouping

Tracking.data_long_2$Age_sex<-NA
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age<=64 & Tracking.data_long_2$Sex == "M"]<-"Males 45-64"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age<=64 & Tracking.data_long_2$Sex == "F"]<-"Females 45-64"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age>64 & Tracking.data_long_2$Sex == "M"]<-"Males 65+"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age>64 & Tracking.data_long_2$Sex == "F"]<-"Females 65+"

6.1) RVLT Immediate Recall

6.1.1) Model

modelRVLT_imm_adj11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + RVLT_Immediate_Normedbaseline + (1 |      ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 105586.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6554 -0.5650 -0.0389  0.5308  3.8582 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.190    2.047   
##  Residual             7.424    2.725   
## Number of obs: 20202, groups:  ID, 10396
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.940e+00
## timefactor2                                                                4.514e-01
## PandemicFU2 data collected before COVID-19                                 8.727e-02
## Age_sexFemales 65+                                                        -1.939e-01
## Age_sexMales 45-64                                                        -6.674e-01
## Age_sexMales 65+                                                          -7.695e-01
## EducationHigh School Diploma                                               2.237e-01
## EducationLess than High School Diploma                                     4.888e-01
## EducationSome College                                                      1.600e-01
## EthnicityWhite                                                             6.292e-01
## IncomeLevel>$150k                                                          7.530e-01
## IncomeLevel$100-150k                                                       6.232e-01
## IncomeLevel$20-50k                                                         2.323e-01
## IncomeLevel$50-100k                                                        5.584e-01
## BMI                                                                       -1.449e-02
## CESD.10baseline                                                           -1.887e-02
## SmokingStatusFormer Smoker                                                 4.244e-02
## SmokingStatusNever Smoked                                                  1.996e-01
## SmokingStatusOccasional Smoker                                            -1.235e-01
## RelationshipstatusMarried                                                  2.452e-01
## RelationshipstatusSeparated                                                1.371e-01
## RelationshipstatusSingle                                                   8.539e-02
## RelationshipstatusWidowed                                                 -1.361e-01
## LivingstatusAssisted Living                                               -1.176e+00
## LivingstatusHouse                                                          1.018e-01
## LivingstatusOther                                                          5.656e-02
## AnxietyYes                                                                 1.429e-02
## MoodDisordYes                                                             -1.325e-01
## Chronicconditions                                                         -4.109e-02
## RVLT_Immediate_Normedbaseline                                              3.463e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.443e-01
## timefactor2:Age_sexFemales 65+                                            -6.221e-01
## timefactor2:Age_sexMales 45-64                                             1.259e-01
## timefactor2:Age_sexMales 65+                                              -5.286e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.210e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -4.254e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -4.750e-03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.022e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  7.147e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    4.193e-01
##                                                                           Std. Error
## (Intercept)                                                                2.996e-01
## timefactor2                                                                1.070e-01
## PandemicFU2 data collected before COVID-19                                 1.201e-01
## Age_sexFemales 65+                                                         1.655e-01
## Age_sexMales 45-64                                                         1.241e-01
## Age_sexMales 65+                                                           1.587e-01
## EducationHigh School Diploma                                               8.746e-02
## EducationLess than High School Diploma                                     1.210e-01
## EducationSome College                                                      1.092e-01
## EthnicityWhite                                                             1.715e-01
## IncomeLevel>$150k                                                          1.644e-01
## IncomeLevel$100-150k                                                       1.306e-01
## IncomeLevel$20-50k                                                         8.555e-02
## IncomeLevel$50-100k                                                        9.242e-02
## BMI                                                                        5.662e-03
## CESD.10baseline                                                            6.930e-03
## SmokingStatusFormer Smoker                                                 1.170e-01
## SmokingStatusNever Smoked                                                  1.222e-01
## SmokingStatusOccasional Smoker                                             2.388e-01
## RelationshipstatusMarried                                                  1.031e-01
## RelationshipstatusSeparated                                                2.011e-01
## RelationshipstatusSingle                                                   1.392e-01
## RelationshipstatusWidowed                                                  1.377e-01
## LivingstatusAssisted Living                                                4.036e-01
## LivingstatusHouse                                                          9.175e-02
## LivingstatusOther                                                          3.149e-01
## AnxietyYes                                                                 1.210e-01
## MoodDisordYes                                                              8.847e-02
## Chronicconditions                                                          1.396e-02
## RVLT_Immediate_Normedbaseline                                              7.412e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.371e-01
## timefactor2:Age_sexFemales 65+                                             1.828e-01
## timefactor2:Age_sexMales 45-64                                             1.396e-01
## timefactor2:Age_sexMales 65+                                               1.800e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.037e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.715e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.028e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.330e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.955e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.324e-01
##                                                                                   df
## (Intercept)                                                                1.093e+04
## timefactor2                                                                1.022e+04
## PandemicFU2 data collected before COVID-19                                 1.798e+04
## Age_sexFemales 65+                                                         1.744e+04
## Age_sexMales 45-64                                                         1.775e+04
## Age_sexMales 65+                                                           1.778e+04
## EducationHigh School Diploma                                               1.033e+04
## EducationLess than High School Diploma                                     1.045e+04
## EducationSome College                                                      1.022e+04
## EthnicityWhite                                                             1.025e+04
## IncomeLevel>$150k                                                          1.030e+04
## IncomeLevel$100-150k                                                       1.026e+04
## IncomeLevel$20-50k                                                         1.032e+04
## IncomeLevel$50-100k                                                        1.031e+04
## BMI                                                                        1.026e+04
## CESD.10baseline                                                            1.032e+04
## SmokingStatusFormer Smoker                                                 1.038e+04
## SmokingStatusNever Smoked                                                  1.037e+04
## SmokingStatusOccasional Smoker                                             1.025e+04
## RelationshipstatusMarried                                                  1.034e+04
## RelationshipstatusSeparated                                                1.040e+04
## RelationshipstatusSingle                                                   1.032e+04
## RelationshipstatusWidowed                                                  1.034e+04
## LivingstatusAssisted Living                                                1.031e+04
## LivingstatusHouse                                                          1.031e+04
## LivingstatusOther                                                          1.021e+04
## AnxietyYes                                                                 1.031e+04
## MoodDisordYes                                                              1.030e+04
## Chronicconditions                                                          1.032e+04
## RVLT_Immediate_Normedbaseline                                              1.032e+04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.013e+04
## timefactor2:Age_sexFemales 65+                                             1.021e+04
## timefactor2:Age_sexMales 45-64                                             1.017e+04
## timefactor2:Age_sexMales 65+                                               1.024e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.799e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.802e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.803e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.016e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.010e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.017e+04
##                                                                           t value
## (Intercept)                                                                23.161
## timefactor2                                                                 4.219
## PandemicFU2 data collected before COVID-19                                  0.727
## Age_sexFemales 65+                                                         -1.172
## Age_sexMales 45-64                                                         -5.378
## Age_sexMales 65+                                                           -4.848
## EducationHigh School Diploma                                                2.558
## EducationLess than High School Diploma                                      4.040
## EducationSome College                                                       1.465
## EthnicityWhite                                                              3.669
## IncomeLevel>$150k                                                           4.579
## IncomeLevel$100-150k                                                        4.773
## IncomeLevel$20-50k                                                          2.716
## IncomeLevel$50-100k                                                         6.041
## BMI                                                                        -2.559
## CESD.10baseline                                                            -2.723
## SmokingStatusFormer Smoker                                                  0.363
## SmokingStatusNever Smoked                                                   1.633
## SmokingStatusOccasional Smoker                                             -0.517
## RelationshipstatusMarried                                                   2.378
## RelationshipstatusSeparated                                                 0.682
## RelationshipstatusSingle                                                    0.613
## RelationshipstatusWidowed                                                  -0.989
## LivingstatusAssisted Living                                                -2.913
## LivingstatusHouse                                                           1.109
## LivingstatusOther                                                           0.180
## AnxietyYes                                                                  0.118
## MoodDisordYes                                                              -1.498
## Chronicconditions                                                          -2.944
## RVLT_Immediate_Normedbaseline                                              46.726
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.052
## timefactor2:Age_sexFemales 65+                                             -3.403
## timefactor2:Age_sexMales 45-64                                              0.902
## timefactor2:Age_sexMales 65+                                               -2.937
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.085
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -0.025
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.023
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.439
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.366
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.804
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               2.47e-05
## PandemicFU2 data collected before COVID-19                                0.467475
## Age_sexFemales 65+                                                        0.241380
## Age_sexMales 45-64                                                        7.63e-08
## Age_sexMales 65+                                                          1.26e-06
## EducationHigh School Diploma                                              0.010549
## EducationLess than High School Diploma                                    5.37e-05
## EducationSome College                                                     0.142845
## EthnicityWhite                                                            0.000245
## IncomeLevel>$150k                                                         4.73e-06
## IncomeLevel$100-150k                                                      1.84e-06
## IncomeLevel$20-50k                                                        0.006619
## IncomeLevel$50-100k                                                       1.58e-09
## BMI                                                                       0.010507
## CESD.10baseline                                                           0.006475
## SmokingStatusFormer Smoker                                                0.716738
## SmokingStatusNever Smoked                                                 0.102471
## SmokingStatusOccasional Smoker                                            0.605148
## RelationshipstatusMarried                                                 0.017412
## RelationshipstatusSeparated                                               0.495475
## RelationshipstatusSingle                                                  0.539735
## RelationshipstatusWidowed                                                 0.322821
## LivingstatusAssisted Living                                               0.003582
## LivingstatusHouse                                                         0.267429
## LivingstatusOther                                                         0.857489
## AnxietyYes                                                                0.905988
## MoodDisordYes                                                             0.134152
## Chronicconditions                                                         0.003244
## RVLT_Immediate_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.292703
## timefactor2:Age_sexFemales 65+                                            0.000669
## timefactor2:Age_sexMales 45-64                                            0.367134
## timefactor2:Age_sexMales 65+                                              0.003324
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.277931
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.980205
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.981310
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.660760
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.714733
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.071256
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                              *  
## EducationLess than High School Diploma                                    ***
## EducationSome College                                                        
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                        ** 
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       *  
## CESD.10baseline                                                           ** 
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 *  
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               ** 
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         ** 
## RVLT_Immediate_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ** 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_imm_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF DenDF   F value    Pr(>F)
## timefactor                      169.6   169.6     1 10155   22.8434 1.782e-06
## Pandemic                         41.8    41.8     1 10343    5.6243 0.0177313
## Age_sex                         946.4   315.5     3 10342   42.4950 < 2.2e-16
## Education                       154.2    51.4     3 10334    6.9225 0.0001188
## Ethnicity                        99.9    99.9     1 10254   13.4582 0.0002452
## IncomeLevel                     385.1    96.3     4 10289   12.9675 1.560e-10
## BMI                              48.6    48.6     1 10256    6.5493 0.0105070
## CESD.10baseline                  55.1    55.1     1 10317    7.4160 0.0064754
## SmokingStatus                    59.5    19.8     3 10297    2.6732 0.0456627
## Relationshipstatus              119.1    29.8     4 10350    4.0096 0.0029827
## Livingstatus                     82.7    27.6     3 10273    3.7143 0.0110062
## Anxiety                           0.1     0.1     1 10310    0.0139 0.9059882
## MoodDisord                       16.7    16.7     1 10304    2.2441 0.1341517
## Chronicconditions                64.4    64.4     1 10319    8.6691 0.0032437
## RVLT_Immediate_Normedbaseline 16208.4 16208.4     1 10321 2183.2965 < 2.2e-16
## timefactor:Pandemic               0.0     0.0     1 10155    0.0023 0.9618127
## timefactor:Age_sex              346.5   115.5     3 10146   15.5582 4.290e-10
## Pandemic:Age_sex                 27.1     9.0     3 10338    1.2164 0.3019797
## timefactor:Pandemic:Age_sex      25.5     8.5     3 10146    1.1449 0.3293676
##                                  
## timefactor                    ***
## Pandemic                      *  
## Age_sex                       ***
## Education                     ***
## Ethnicity                     ***
## IncomeLevel                   ***
## BMI                           *  
## CESD.10baseline               ** 
## SmokingStatus                 *  
## Relationshipstatus            ** 
## Livingstatus                  *  
## Anxiety                          
## MoodDisord                       
## Chronicconditions             ** 
## RVLT_Immediate_Normedbaseline ***
## timefactor:Pandemic              
## timefactor:Age_sex            ***
## Pandemic:Age_sex                 
## timefactor:Pandemic:Age_sex      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

6.1.2) Estimated marginal means

Significantly lower RVLT immediate for males 65+

lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.707067 0.2004231 11780.92 10.314204
##  FU2 data collected before COVID-19 10.794333 0.1938650 11311.37 10.414323
##  upper.CL
##  11.09993
##  11.17434
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  11.158480 0.2010575 11870.42 10.764375
##  FU2 data collected before COVID-19 11.101445 0.1938384 11307.39 10.721489
##  upper.CL
##  11.55259
##  11.48140
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.513168 0.2202030 12710.13 10.081537
##  FU2 data collected before COVID-19 10.821400 0.2078429 11986.07 10.413994
##  upper.CL
##  10.94480
##  11.22881
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.342444 0.2217520 12905.48  9.907777
##  FU2 data collected before COVID-19 10.608615 0.2078874 11992.17 10.201122
##  upper.CL
##  10.77711
##  11.01611
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.039686 0.1925409 11407.67  9.662273
##  FU2 data collected before COVID-19 10.122698 0.2008412 11818.30  9.729016
##  upper.CL
##  10.41710
##  10.51638
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.616965 0.1927993 11445.63 10.239045
##  FU2 data collected before COVID-19 10.627146 0.2006770 11795.18 10.233786
##  upper.CL
##  10.99488
##  11.02051
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.937579 0.2203955 12593.93  9.505570
##  FU2 data collected before COVID-19 10.020094 0.2077153 12095.66  9.612939
##  upper.CL
##  10.36959
##  10.42725
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.860365 0.2221716 12819.42  9.424876
##  FU2 data collected before COVID-19 10.217875 0.2075553 12074.17  9.811033
##  upper.CL
##  10.29585
##  10.62472
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0872661 0.1201007 17983.01  -0.727  0.4675
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   0.0570350 0.1213607 18147.63   0.470  0.6384
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3082322 0.1647136 17973.79  -1.871  0.0613
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2661712 0.1670098 18191.34  -1.594  0.1110
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0830116 0.1228335 18004.38  -0.676  0.4992
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0101808 0.1230316 18030.11  -0.083  0.9341
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0825158 0.1635775 18041.98  -0.504  0.6140
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3575100 0.1656631 18238.08  -2.158  0.0309
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.0872661 0.1201007 17983.01 -0.3226749  0.14814283
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##   0.0570350 0.1213607 18147.63 -0.1808435  0.29491356
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.3082322 0.1647136 17973.79 -0.6310867  0.01462228
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.2661712 0.1670098 18191.34 -0.5935262  0.06118384
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.0830116 0.1228335 18004.38 -0.3237770  0.15775381
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.0101808 0.1230316 18030.11 -0.2513345  0.23097289
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.0825158 0.1635775 18041.98 -0.4031434  0.23811169
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.3575100 0.1656631 18238.08 -0.6822254 -0.03279466
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

6.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj11 <- summary(lsmeans(modelRVLT_imm_adj11, ~timefactor|Pandemic|Age_sex))
RVLTimmediate_lsmeans_adj11$Time<-NA
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

6.1.4) Post-hoc comparisons

There was a significant difference between cohorts for males 65+. This post-hoc comparison determines if differences between cohorts at FU1 and FU2 for 65+ males are significant.

lsmeans.RVLT_imm <- lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex)
contrast(lsmeans.RVLT_imm,list(c2nd),by=NULL)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##   estimate        SE      df t.ratio p.value
##  0.2749942 0.1876556 10201.1   1.465  0.1428
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger

6.2) RVLT Delayed Recall

6.2.1) Model

modelRVLT_del_adj11<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + RVLT_Delayed_Normedbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 103592.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9455 -0.5503 -0.0372  0.5096  4.1361 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.124    2.031   
##  Residual             6.922    2.631   
## Number of obs: 20030, groups:  ID, 10379
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.432e+00
## timefactor2                                                                8.114e-01
## PandemicFU2 data collected before COVID-19                                 2.055e-01
## Age_sexFemales 65+                                                         5.190e-02
## Age_sexMales 45-64                                                        -4.873e-01
## Age_sexMales 65+                                                          -4.201e-01
## EducationHigh School Diploma                                               3.130e-01
## EducationLess than High School Diploma                                     3.171e-01
## EducationSome College                                                      2.128e-01
## EthnicityWhite                                                             7.425e-01
## IncomeLevel>$150k                                                          6.213e-01
## IncomeLevel$100-150k                                                       4.775e-01
## IncomeLevel$20-50k                                                         2.284e-01
## IncomeLevel$50-100k                                                        5.666e-01
## BMI                                                                       -1.501e-02
## CESD.10baseline                                                           -1.914e-02
## SmokingStatusFormer Smoker                                                 3.034e-02
## SmokingStatusNever Smoked                                                  2.564e-01
## SmokingStatusOccasional Smoker                                             1.547e-02
## RelationshipstatusMarried                                                  2.288e-02
## RelationshipstatusSeparated                                               -1.112e-01
## RelationshipstatusSingle                                                  -4.383e-02
## RelationshipstatusWidowed                                                 -3.012e-01
## LivingstatusAssisted Living                                               -1.316e+00
## LivingstatusHouse                                                          1.529e-01
## LivingstatusOther                                                         -2.964e-02
## AnxietyYes                                                                 9.757e-02
## MoodDisordYes                                                             -1.488e-01
## Chronicconditions                                                         -5.269e-02
## RVLT_Delayed_Normedbaseline                                                3.915e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -3.751e-01
## timefactor2:Age_sexFemales 65+                                            -6.707e-01
## timefactor2:Age_sexMales 45-64                                            -9.392e-03
## timefactor2:Age_sexMales 65+                                              -8.039e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.443e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.362e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -8.744e-03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.601e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.681e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    5.630e-01
##                                                                           Std. Error
## (Intercept)                                                                2.961e-01
## timefactor2                                                                1.036e-01
## PandemicFU2 data collected before COVID-19                                 1.174e-01
## Age_sexFemales 65+                                                         1.621e-01
## Age_sexMales 45-64                                                         1.215e-01
## Age_sexMales 65+                                                           1.559e-01
## EducationHigh School Diploma                                               8.594e-02
## EducationLess than High School Diploma                                     1.193e-01
## EducationSome College                                                      1.072e-01
## EthnicityWhite                                                             1.692e-01
## IncomeLevel>$150k                                                          1.614e-01
## IncomeLevel$100-150k                                                       1.283e-01
## IncomeLevel$20-50k                                                         8.412e-02
## IncomeLevel$50-100k                                                        9.081e-02
## BMI                                                                        5.557e-03
## CESD.10baseline                                                            6.813e-03
## SmokingStatusFormer Smoker                                                 1.148e-01
## SmokingStatusNever Smoked                                                  1.200e-01
## SmokingStatusOccasional Smoker                                             2.347e-01
## RelationshipstatusMarried                                                  1.014e-01
## RelationshipstatusSeparated                                                1.977e-01
## RelationshipstatusSingle                                                   1.368e-01
## RelationshipstatusWidowed                                                  1.353e-01
## LivingstatusAssisted Living                                                3.978e-01
## LivingstatusHouse                                                          9.017e-02
## LivingstatusOther                                                          3.106e-01
## AnxietyYes                                                                 1.188e-01
## MoodDisordYes                                                              8.683e-02
## Chronicconditions                                                          1.371e-02
## RVLT_Delayed_Normedbaseline                                                7.465e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.328e-01
## timefactor2:Age_sexFemales 65+                                             1.781e-01
## timefactor2:Age_sexMales 45-64                                             1.353e-01
## timefactor2:Age_sexMales 65+                                               1.754e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.992e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.677e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.991e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.265e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.896e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.263e-01
##                                                                                   df
## (Intercept)                                                                1.093e+04
## timefactor2                                                                1.010e+04
## PandemicFU2 data collected before COVID-19                                 1.774e+04
## Age_sexFemales 65+                                                         1.721e+04
## Age_sexMales 45-64                                                         1.755e+04
## Age_sexMales 65+                                                           1.761e+04
## EducationHigh School Diploma                                               1.031e+04
## EducationLess than High School Diploma                                     1.050e+04
## EducationSome College                                                      1.020e+04
## EthnicityWhite                                                             1.034e+04
## IncomeLevel>$150k                                                          1.028e+04
## IncomeLevel$100-150k                                                       1.027e+04
## IncomeLevel$20-50k                                                         1.033e+04
## IncomeLevel$50-100k                                                        1.029e+04
## BMI                                                                        1.022e+04
## CESD.10baseline                                                            1.033e+04
## SmokingStatusFormer Smoker                                                 1.034e+04
## SmokingStatusNever Smoked                                                  1.033e+04
## SmokingStatusOccasional Smoker                                             1.027e+04
## RelationshipstatusMarried                                                  1.029e+04
## RelationshipstatusSeparated                                                1.041e+04
## RelationshipstatusSingle                                                   1.027e+04
## RelationshipstatusWidowed                                                  1.031e+04
## LivingstatusAssisted Living                                                1.041e+04
## LivingstatusHouse                                                          1.031e+04
## LivingstatusOther                                                          1.033e+04
## AnxietyYes                                                                 1.028e+04
## MoodDisordYes                                                              1.027e+04
## Chronicconditions                                                          1.030e+04
## RVLT_Delayed_Normedbaseline                                                1.028e+04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.001e+04
## timefactor2:Age_sexFemales 65+                                             1.017e+04
## timefactor2:Age_sexMales 45-64                                             1.008e+04
## timefactor2:Age_sexMales 65+                                               1.021e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.776e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.780e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.785e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.010e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.000e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.013e+04
##                                                                           t value
## (Intercept)                                                                21.725
## timefactor2                                                                 7.833
## PandemicFU2 data collected before COVID-19                                  1.750
## Age_sexFemales 65+                                                          0.320
## Age_sexMales 45-64                                                         -4.012
## Age_sexMales 65+                                                           -2.695
## EducationHigh School Diploma                                                3.642
## EducationLess than High School Diploma                                      2.658
## EducationSome College                                                       1.985
## EthnicityWhite                                                              4.388
## IncomeLevel>$150k                                                           3.850
## IncomeLevel$100-150k                                                        3.721
## IncomeLevel$20-50k                                                          2.715
## IncomeLevel$50-100k                                                         6.239
## BMI                                                                        -2.701
## CESD.10baseline                                                            -2.810
## SmokingStatusFormer Smoker                                                  0.264
## SmokingStatusNever Smoked                                                   2.137
## SmokingStatusOccasional Smoker                                              0.066
## RelationshipstatusMarried                                                   0.226
## RelationshipstatusSeparated                                                -0.562
## RelationshipstatusSingle                                                   -0.320
## RelationshipstatusWidowed                                                  -2.226
## LivingstatusAssisted Living                                                -3.308
## LivingstatusHouse                                                           1.695
## LivingstatusOther                                                          -0.095
## AnxietyYes                                                                  0.821
## MoodDisordYes                                                              -1.714
## Chronicconditions                                                          -3.843
## RVLT_Delayed_Normedbaseline                                                52.451
## timefactor2:PandemicFU2 data collected before COVID-19                     -2.826
## timefactor2:Age_sexFemales 65+                                             -3.765
## timefactor2:Age_sexMales 45-64                                             -0.069
## timefactor2:Age_sexMales 65+                                               -4.584
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.226
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.008
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.044
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.707
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.141
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     2.488
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               5.26e-15
## PandemicFU2 data collected before COVID-19                                0.080063
## Age_sexFemales 65+                                                        0.748826
## Age_sexMales 45-64                                                        6.05e-05
## Age_sexMales 65+                                                          0.007039
## EducationHigh School Diploma                                              0.000272
## EducationLess than High School Diploma                                    0.007871
## EducationSome College                                                     0.047155
## EthnicityWhite                                                            1.15e-05
## IncomeLevel>$150k                                                         0.000119
## IncomeLevel$100-150k                                                      0.000200
## IncomeLevel$20-50k                                                        0.006633
## IncomeLevel$50-100k                                                       4.57e-10
## BMI                                                                       0.006920
## CESD.10baseline                                                           0.004963
## SmokingStatusFormer Smoker                                                0.791610
## SmokingStatusNever Smoked                                                 0.032650
## SmokingStatusOccasional Smoker                                            0.947428
## RelationshipstatusMarried                                                 0.821385
## RelationshipstatusSeparated                                               0.573925
## RelationshipstatusSingle                                                  0.748603
## RelationshipstatusWidowed                                                 0.026066
## LivingstatusAssisted Living                                               0.000942
## LivingstatusHouse                                                         0.090048
## LivingstatusOther                                                         0.923984
## AnxietyYes                                                                0.411648
## MoodDisordYes                                                             0.086573
## Chronicconditions                                                         0.000122
## RVLT_Delayed_Normedbaseline                                                < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.004729
## timefactor2:Age_sexFemales 65+                                            0.000167
## timefactor2:Age_sexMales 45-64                                            0.944675
## timefactor2:Age_sexMales 65+                                              4.61e-06
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.220088
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.993521
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.964962
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.479580
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.887544
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.012861
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                .  
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ** 
## EducationHigh School Diploma                                              ***
## EducationLess than High School Diploma                                    ** 
## EducationSome College                                                     *  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                        ** 
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.10baseline                                                           ** 
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 *  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                 *  
## LivingstatusAssisted Living                                               ***
## LivingstatusHouse                                                         .  
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                             .  
## Chronicconditions                                                         ***
## RVLT_Delayed_Normedbaseline                                               ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_del_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF DenDF   F value    Pr(>F)    
## timefactor                    495.8   495.8     1 10110   71.6357 < 2.2e-16 ***
## Pandemic                       53.7    53.7     1 10353    7.7579 0.0053574 ** 
## Age_sex                       581.0   193.7     3 10338   27.9783 < 2.2e-16 ***
## Education                     134.8    44.9     3 10338    6.4931 0.0002194 ***
## Ethnicity                     133.3   133.3     1 10338   19.2584 1.153e-05 ***
## IncomeLevel                   331.7    82.9     4 10277   11.9811 1.030e-09 ***
## BMI                            50.5    50.5     1 10216    7.2965 0.0069202 ** 
## CESD.10baseline                54.7    54.7     1 10331    7.8963 0.0049628 ** 
## SmokingStatus                 101.7    33.9     3 10290    4.8962 0.0021128 ** 
## Relationshipstatus             65.5    16.4     4 10330    2.3645 0.0506887 .  
## Livingstatus                  113.5    37.8     3 10351    5.4662 0.0009447 ***
## Anxiety                         4.7     4.7     1 10283    0.6741 0.4116479    
## MoodDisord                     20.3    20.3     1 10273    2.9375 0.0865734 .  
## Chronicconditions             102.2   102.2     1 10297   14.7689 0.0001223 ***
## RVLT_Delayed_Normedbaseline 19042.0 19042.0     1 10284 2751.1263 < 2.2e-16 ***
## timefactor:Pandemic            43.4    43.4     1 10110    6.2664 0.0123206 *  
## timefactor:Age_sex            321.5   107.2     3 10093   15.4821 4.797e-10 ***
## Pandemic:Age_sex               47.0    15.7     3 10340    2.2614 0.0791669 .  
## timefactor:Pandemic:Age_sex    54.6    18.2     3 10093    2.6285 0.0484988 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

6.2.2) Estimated marginal means

Significantly lower scores at FU2 for males 65+

lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.60914 0.1971469 11773.94 10.222696
##  FU2 data collected before COVID-19 10.81460 0.1906861 11314.11 10.440822
##  upper.CL
##  10.99558
##  11.18838
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  11.42052 0.1975804 11836.41 11.033232
##  FU2 data collected before COVID-19 11.25086 0.1906684 11311.34 10.877116
##  upper.CL
##  11.80781
##  11.62460
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.66104 0.2163796 12651.03 10.236902
##  FU2 data collected before COVID-19 11.11081 0.2043714 11957.88 10.710209
##  upper.CL
##  11.08518
##  11.51141
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.80169 0.2187777 12960.17 10.372849
##  FU2 data collected before COVID-19 11.03647 0.2044319 11966.28 10.635748
##  upper.CL
##  11.23052
##  11.43719
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.12180 0.1894798 11433.08  9.750385
##  FU2 data collected before COVID-19 10.32862 0.1974057 11795.92  9.941674
##  upper.CL
##  10.49321
##  10.71557
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.92379 0.1895707 11446.90 10.552200
##  FU2 data collected before COVID-19 10.72868 0.1973740 11791.24 10.341798
##  upper.CL
##  11.29538
##  11.11557
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.18902 0.2169246 12593.69  9.763816
##  FU2 data collected before COVID-19 10.38574 0.2045931 12121.30  9.984704
##  upper.CL
##  10.61423
##  10.78678
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  10.19650 0.2184803 12795.20  9.768243
##  FU2 data collected before COVID-19 10.58112 0.2040985 12053.06 10.181052
##  upper.CL
##  10.62475
##  10.98118
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2054627 0.1173793 17734.40  -1.750  0.0801
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   0.1696624 0.1185313 17894.94   1.431  0.1523
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.4497714 0.1611727 17744.06  -2.791  0.0053
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2347825 0.1641374 18037.41  -1.430  0.1526
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2068246 0.1202199 17793.45  -1.720  0.0854
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   0.1951070 0.1203034 17804.95   1.622  0.1049
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1967185 0.1609259 17895.46  -1.222  0.2216
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3846216 0.1624764 18046.87  -2.367  0.0179
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.2054627 0.1173793 17734.40 -0.4355376  0.0246121
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##   0.1696624 0.1185313 17894.94 -0.0626705  0.4019953
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.4497714 0.1611727 17744.06 -0.7656856 -0.1338571
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.2347825 0.1641374 18037.41 -0.5565075  0.0869426
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.2068246 0.1202199 17793.45 -0.4424674  0.0288181
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##   0.1951070 0.1203034 17804.95 -0.0406994  0.4309133
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.1967185 0.1609259 17895.46 -0.5121488  0.1187117
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.3846216 0.1624764 18046.87 -0.7030909 -0.0661523
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

6.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj11 <- summary(lsmeans(modelRVLT_del_adj11, ~timefactor|Pandemic|Age_sex))
RVLTdelayed_lsmeans_adj11$Time<-NA
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

6.2.4) Post-hoc comparisons

There was a significant difference between cohorts for males 65+. This post-hoc comparison determines if differences between cohorts at FU1 and FU2 for 65+ males are significant.

lsmeans.RVLT_imm <- lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex)
contrast(lsmeans.RVLT_imm,list(c2nd),by=NULL)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##   estimate        SE       df t.ratio p.value
##  0.1879031 0.1832561 10183.52   1.025  0.3052
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans.RVLT_imm,list(c2nd),by=NULL), parm, level = 0.95)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##   estimate        SE       df   lower.CL  upper.CL
##  0.1879031 0.1832561 10183.52 -0.1713149 0.5471211
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

6.3) Mental Alteration Test

6.3.1) Model

modelMAT_adj11<- lmer(MAT_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + MAT_Normedbaseline +
                      (1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     MAT_Normedbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 97258.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4157 -0.5134 -0.0505  0.3918  4.6109 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.617    1.618   
##  Residual             7.857    2.803   
## Number of obs: 18841, groups:  ID, 10262
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.506e+00
## timefactor2                                                               -3.390e+00
## PandemicFU2 data collected before COVID-19                                -1.609e+00
## Age_sexFemales 65+                                                        -9.236e-01
## Age_sexMales 45-64                                                        -3.457e+00
## Age_sexMales 65+                                                          -3.492e+00
## EducationHigh School Diploma                                              -1.927e-02
## EducationLess than High School Diploma                                    -1.655e-01
## EducationSome College                                                     -1.753e-01
## EthnicityWhite                                                             8.811e-01
## IncomeLevel>$150k                                                          1.679e-01
## IncomeLevel$100-150k                                                       1.954e-01
## IncomeLevel$20-50k                                                         9.886e-02
## IncomeLevel$50-100k                                                        1.481e-01
## BMI                                                                       -1.358e-02
## CESD.10baseline                                                           -1.386e-02
## SmokingStatusFormer Smoker                                                 4.959e-02
## SmokingStatusNever Smoked                                                 -5.464e-02
## SmokingStatusOccasional Smoker                                             1.019e-01
## RelationshipstatusMarried                                                  9.269e-03
## RelationshipstatusSeparated                                               -9.687e-02
## RelationshipstatusSingle                                                   4.971e-01
## RelationshipstatusWidowed                                                 -6.718e-02
## LivingstatusAssisted Living                                               -2.840e-01
## LivingstatusHouse                                                         -2.156e-01
## LivingstatusOther                                                         -7.398e-01
## AnxietyYes                                                                 1.348e-01
## MoodDisordYes                                                              7.383e-03
## Chronicconditions                                                         -4.312e-02
## MAT_Normedbaseline                                                         4.406e-01
## timefactor2:PandemicFU2 data collected before COVID-19                     1.731e+00
## timefactor2:Age_sexFemales 65+                                             7.108e-01
## timefactor2:Age_sexMales 45-64                                             3.445e+00
## timefactor2:Age_sexMales 65+                                               3.076e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.558e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.688e+00
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.907e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  4.954e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.807e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -1.746e+00
##                                                                           Std. Error
## (Intercept)                                                                2.835e-01
## timefactor2                                                                1.131e-01
## PandemicFU2 data collected before COVID-19                                 1.170e-01
## Age_sexFemales 65+                                                         1.640e-01
## Age_sexMales 45-64                                                         1.212e-01
## Age_sexMales 65+                                                           1.584e-01
## EducationHigh School Diploma                                               8.200e-02
## EducationLess than High School Diploma                                     1.153e-01
## EducationSome College                                                      1.016e-01
## EthnicityWhite                                                             1.621e-01
## IncomeLevel>$150k                                                          1.537e-01
## IncomeLevel$100-150k                                                       1.223e-01
## IncomeLevel$20-50k                                                         8.052e-02
## IncomeLevel$50-100k                                                        8.689e-02
## BMI                                                                        5.295e-03
## CESD.10baseline                                                            6.498e-03
## SmokingStatusFormer Smoker                                                 1.093e-01
## SmokingStatusNever Smoked                                                  1.142e-01
## SmokingStatusOccasional Smoker                                             2.235e-01
## RelationshipstatusMarried                                                  9.682e-02
## RelationshipstatusSeparated                                                1.877e-01
## RelationshipstatusSingle                                                   1.305e-01
## RelationshipstatusWidowed                                                  1.299e-01
## LivingstatusAssisted Living                                                3.819e-01
## LivingstatusHouse                                                          8.622e-02
## LivingstatusOther                                                          2.963e-01
## AnxietyYes                                                                 1.132e-01
## MoodDisordYes                                                              8.263e-02
## Chronicconditions                                                          1.314e-02
## MAT_Normedbaseline                                                         7.681e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.449e-01
## timefactor2:Age_sexFemales 65+                                             1.985e-01
## timefactor2:Age_sexMales 45-64                                             1.479e-01
## timefactor2:Age_sexMales 65+                                               1.968e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.015e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.677e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.015e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.514e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.077e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.527e-01
##                                                                                   df
## (Intercept)                                                                1.081e+04
## timefactor2                                                                9.540e+03
## PandemicFU2 data collected before COVID-19                                 1.799e+04
## Age_sexFemales 65+                                                         1.774e+04
## Age_sexMales 45-64                                                         1.784e+04
## Age_sexMales 65+                                                           1.794e+04
## EducationHigh School Diploma                                               9.997e+03
## EducationLess than High School Diploma                                     1.027e+04
## EducationSome College                                                      9.863e+03
## EthnicityWhite                                                             1.016e+04
## IncomeLevel>$150k                                                          9.938e+03
## IncomeLevel$100-150k                                                       9.958e+03
## IncomeLevel$20-50k                                                         1.006e+04
## IncomeLevel$50-100k                                                        9.994e+03
## BMI                                                                        9.851e+03
## CESD.10baseline                                                            9.960e+03
## SmokingStatusFormer Smoker                                                 9.996e+03
## SmokingStatusNever Smoked                                                  9.990e+03
## SmokingStatusOccasional Smoker                                             9.882e+03
## RelationshipstatusMarried                                                  1.008e+04
## RelationshipstatusSeparated                                                1.004e+04
## RelationshipstatusSingle                                                   1.003e+04
## RelationshipstatusWidowed                                                  1.015e+04
## LivingstatusAssisted Living                                                1.018e+04
## LivingstatusHouse                                                          1.007e+04
## LivingstatusOther                                                          9.881e+03
## AnxietyYes                                                                 9.993e+03
## MoodDisordYes                                                              9.931e+03
## Chronicconditions                                                          1.010e+04
## MAT_Normedbaseline                                                         1.006e+04
## timefactor2:PandemicFU2 data collected before COVID-19                     9.435e+03
## timefactor2:Age_sexFemales 65+                                             9.927e+03
## timefactor2:Age_sexMales 45-64                                             9.521e+03
## timefactor2:Age_sexMales 65+                                               9.955e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.807e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.804e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.809e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  9.785e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  9.477e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    9.829e+03
##                                                                           t value
## (Intercept)                                                                30.001
## timefactor2                                                               -29.976
## PandemicFU2 data collected before COVID-19                                -13.752
## Age_sexFemales 65+                                                         -5.632
## Age_sexMales 45-64                                                        -28.522
## Age_sexMales 65+                                                          -22.054
## EducationHigh School Diploma                                               -0.235
## EducationLess than High School Diploma                                     -1.434
## EducationSome College                                                      -1.724
## EthnicityWhite                                                              5.435
## IncomeLevel>$150k                                                           1.092
## IncomeLevel$100-150k                                                        1.597
## IncomeLevel$20-50k                                                          1.228
## IncomeLevel$50-100k                                                         1.704
## BMI                                                                        -2.566
## CESD.10baseline                                                            -2.133
## SmokingStatusFormer Smoker                                                  0.454
## SmokingStatusNever Smoked                                                  -0.478
## SmokingStatusOccasional Smoker                                              0.456
## RelationshipstatusMarried                                                   0.096
## RelationshipstatusSeparated                                                -0.516
## RelationshipstatusSingle                                                    3.809
## RelationshipstatusWidowed                                                  -0.517
## LivingstatusAssisted Living                                                -0.743
## LivingstatusHouse                                                          -2.500
## LivingstatusOther                                                          -2.497
## AnxietyYes                                                                  1.191
## MoodDisordYes                                                               0.089
## Chronicconditions                                                          -3.283
## MAT_Normedbaseline                                                         57.359
## timefactor2:PandemicFU2 data collected before COVID-19                     11.942
## timefactor2:Age_sexFemales 65+                                              3.580
## timefactor2:Age_sexMales 45-64                                             23.287
## timefactor2:Age_sexMales 65+                                               15.632
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.270
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              10.062
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 9.468
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.197
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -8.703
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -6.908
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                 < 2e-16
## Age_sexFemales 65+                                                        1.81e-08
## Age_sexMales 45-64                                                         < 2e-16
## Age_sexMales 65+                                                           < 2e-16
## EducationHigh School Diploma                                              0.814188
## EducationLess than High School Diploma                                    0.151475
## EducationSome College                                                     0.084658
## EthnicityWhite                                                            5.61e-08
## IncomeLevel>$150k                                                         0.274718
## IncomeLevel$100-150k                                                      0.110201
## IncomeLevel$20-50k                                                        0.219560
## IncomeLevel$50-100k                                                       0.088339
## BMI                                                                       0.010311
## CESD.10baseline                                                           0.032911
## SmokingStatusFormer Smoker                                                0.650015
## SmokingStatusNever Smoked                                                 0.632337
## SmokingStatusOccasional Smoker                                            0.648399
## RelationshipstatusMarried                                                 0.923737
## RelationshipstatusSeparated                                               0.605872
## RelationshipstatusSingle                                                  0.000140
## RelationshipstatusWidowed                                                 0.604957
## LivingstatusAssisted Living                                               0.457219
## LivingstatusHouse                                                         0.012419
## LivingstatusOther                                                         0.012544
## AnxietyYes                                                                0.233577
## MoodDisordYes                                                             0.928802
## Chronicconditions                                                         0.001031
## MAT_Normedbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     < 2e-16
## timefactor2:Age_sexFemales 65+                                            0.000345
## timefactor2:Age_sexMales 45-64                                             < 2e-16
## timefactor2:Age_sexMales 65+                                               < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.204226
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.843808
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   5.24e-12
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                     .  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       .  
## BMI                                                                       *  
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                  ***
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         *  
## LivingstatusOther                                                         *  
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         ** 
## MAT_Normedbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                            ***
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             ***
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelMAT_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF   DenDF   F value    Pr(>F)
## timefactor                   5230.1  5230.1     1  9817.9  665.6614 < 2.2e-16
## Pandemic                      117.9   117.9     1 10185.2   15.0061 0.0001078
## Age_sex                      3898.4  1299.5     3 10141.0  165.3911 < 2.2e-16
## Education                      35.9    12.0     3 10043.7    1.5248 0.2058103
## Ethnicity                     232.1   232.1     1 10164.4   29.5375 5.611e-08
## IncomeLevel                    28.6     7.2     4  9964.1    0.9105 0.4566491
## BMI                            51.7    51.7     1  9851.2    6.5829 0.0103112
## CESD.10baseline                35.8    35.8     1  9960.1    4.5517 0.0329111
## SmokingStatus                  26.7     8.9     3  9942.4    1.1345 0.3335396
## Relationshipstatus            200.0    50.0     4 10062.3    6.3652 4.120e-05
## Livingstatus                   79.9    26.6     3 10044.7    3.3909 0.0171896
## Anxiety                        11.2    11.2     1  9993.4    1.4191 0.2335774
## MoodDisord                      0.1     0.1     1  9930.9    0.0080 0.9288023
## Chronicconditions              84.7    84.7     1 10095.4   10.7777 0.0010308
## MAT_Normedbaseline          25849.9 25849.9     1 10062.7 3290.0323 < 2.2e-16
## timefactor:Pandemic           716.3   716.3     1  9818.4   91.1717 < 2.2e-16
## timefactor:Age_sex           5550.9  1850.3     3  9759.5  235.4981 < 2.2e-16
## Pandemic:Age_sex              458.7   152.9     3 10146.3   19.4623 1.409e-12
## timefactor:Pandemic:Age_sex   893.1   297.7     3  9759.5   37.8895 < 2.2e-16
##                                
## timefactor                  ***
## Pandemic                    ***
## Age_sex                     ***
## Education                      
## Ethnicity                   ***
## IncomeLevel                    
## BMI                         *  
## CESD.10baseline             *  
## SmokingStatus                  
## Relationshipstatus          ***
## Livingstatus                *  
## Anxiety                        
## MoodDisord                     
## Chronicconditions           ** 
## MAT_Normedbaseline          ***
## timefactor:Pandemic         ***
## timefactor:Age_sex          ***
## Pandemic:Age_sex            ***
## timefactor:Pandemic:Age_sex ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

6.3.2) Estimated marginal means

Significantly lower MAT score for males and females 65+

lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  12.696519 0.1905041 11855.95 12.323099
##  FU2 data collected before COVID-19 11.087143 0.1837216 11296.31 10.727017
##   upper.CL
##  13.069938
##  11.447269
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.306276 0.1907101 11881.60  8.932453
##  FU2 data collected before COVID-19  9.427605 0.1834602 11260.94  9.067991
##   upper.CL
##   9.680099
##   9.787219
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19  11.772876 0.2121837 13142.93 11.356965
##  FU2 data collected before COVID-19 10.419276 0.1976457 12091.40 10.031859
##   upper.CL
##  12.188787
##  10.806694
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.093475 0.2130980 13240.43  8.675772
##  FU2 data collected before COVID-19  9.520122 0.1978095 12111.32  9.132383
##   upper.CL
##   9.511177
##   9.907860
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.239715 0.1827928 11440.94  8.881410
##  FU2 data collected before COVID-19  9.317922 0.1910888 11929.91  8.943357
##   upper.CL
##   9.598020
##   9.692487
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.294013 0.1826239 11418.77  8.936039
##  FU2 data collected before COVID-19  9.295575 0.1908607 11901.46  8.921457
##   upper.CL
##   9.651987
##   9.669693
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   9.204070 0.2130949 13006.41  8.786373
##  FU2 data collected before COVID-19  9.502097 0.1984528 12277.69  9.113098
##   upper.CL
##   9.621768
##   9.891095
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE       df  lower.CL
##  FU2 data collected after COVID-19   8.890290 0.2151540 13226.18  8.468558
##  FU2 data collected before COVID-19  9.173133 0.1986219 12298.67  8.783803
##   upper.CL
##   9.312023
##   9.562463
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   1.6093755 0.1170264 17996.69  13.752  <.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1213285 0.1175053 18025.46  -1.033  0.3018
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   1.3535998 0.1641553 18093.83   8.246  <.0001
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.4266467 0.1663959 18178.31  -2.564  0.0104
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0782070 0.1203861 18053.11  -0.650  0.5159
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0015620 0.1199586 18028.66  -0.013  0.9896
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2980262 0.1641423 18122.43  -1.816  0.0694
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2828423 0.1675609 18244.13  -1.688  0.0914
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##   1.6093755 0.1170264 17996.69  1.3799926  1.8387584
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.1213285 0.1175053 18025.46 -0.3516502  0.1089931
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##   1.3535998 0.1641553 18093.83  1.0318397  1.6753598
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.4266467 0.1663959 18178.31 -0.7527985 -0.1004949
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.0782070 0.1203861 18053.11 -0.3141752  0.1577612
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.0015620 0.1199586 18028.66 -0.2366923  0.2335683
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.2980262 0.1641423 18122.43 -0.6197607  0.0237083
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL   upper.CL
##  -0.2828423 0.1675609 18244.13 -0.6112773  0.0455928
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

6.3.3) Graph of estimated marginal means

MAT_lsmeans_adj11 <- summary(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex))
MAT_lsmeans_adj11$Time<-NA
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

6.3.4) Post-hoc comparisons

There was a significant difference between cohorts for males and females 65+. This post-hoc comparison determines if differences between cohorts at FU1 and FU2 for 65+ males and females are significant.

lsmeans.MAT <- lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex)
contrast(lsmeans.MAT,list(c2nd, c3rd),by=NULL)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##  structure(c(0, 0, 0, 0, 1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##    estimate        SE       df t.ratio p.value
##  -0.0151839 0.2070694 10043.53  -0.073  0.9415
##   1.7802465 0.2054848  9979.28   8.664  <.0001
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans.MAT,list(c2nd, c3rd),by=NULL), parm, level = 0.95)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##  structure(c(0, 0, 0, 0, 1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##    estimate        SE       df   lower.CL  upper.CL
##  -0.0151839 0.2070694 10043.53 -0.4210814 0.3907137
##   1.7802465 0.2054848  9979.28  1.3774549 2.1830381
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

6.4) Animal Fluency

6.4.1) Model

modelAnimals_adj11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + Animal_Fluency_Normedbaseline +
                           (1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + Animal_Fluency_Normedbaseline + (1 |      ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 94883.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9084 -0.5456 -0.0177  0.5260  4.5905 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 3.012    1.735   
##  Residual             3.861    1.965   
## Number of obs: 20333, groups:  ID, 10403
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                4.188e+00
## timefactor2                                                                2.776e-01
## PandemicFU2 data collected before COVID-19                                 2.798e-01
## Age_sexFemales 65+                                                        -3.304e-01
## Age_sexMales 45-64                                                         6.509e-02
## Age_sexMales 65+                                                          -2.951e-01
## EducationHigh School Diploma                                               2.178e-01
## EducationLess than High School Diploma                                     5.810e-02
## EducationSome College                                                      2.113e-01
## EthnicityWhite                                                             5.876e-01
## IncomeLevel>$150k                                                          1.545e-01
## IncomeLevel$100-150k                                                       2.370e-01
## IncomeLevel$20-50k                                                        -1.204e-02
## IncomeLevel$50-100k                                                        1.912e-01
## BMI                                                                       -3.383e-03
## CESD.10baseline                                                           -1.300e-02
## SmokingStatusFormer Smoker                                                 7.776e-02
## SmokingStatusNever Smoked                                                  7.149e-02
## SmokingStatusOccasional Smoker                                            -6.197e-02
## RelationshipstatusMarried                                                  8.270e-02
## RelationshipstatusSeparated                                                2.942e-01
## RelationshipstatusSingle                                                   7.561e-02
## RelationshipstatusWidowed                                                 -4.134e-02
## LivingstatusAssisted Living                                               -4.008e-02
## LivingstatusHouse                                                          2.280e-01
## LivingstatusOther                                                         -3.095e-02
## AnxietyYes                                                                 6.147e-02
## MoodDisordYes                                                              9.588e-02
## Chronicconditions                                                         -2.937e-02
## Animal_Fluency_Normedbaseline                                              5.559e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.142e-01
## timefactor2:Age_sexFemales 65+                                            -3.811e-01
## timefactor2:Age_sexMales 45-64                                            -2.128e-01
## timefactor2:Age_sexMales 65+                                              -5.271e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              6.595e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -3.373e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -1.274e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  4.552e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.006e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.572e-01
##                                                                           Std. Error
## (Intercept)                                                                2.370e-01
## timefactor2                                                                7.709e-02
## PandemicFU2 data collected before COVID-19                                 9.222e-02
## Age_sexFemales 65+                                                         1.271e-01
## Age_sexMales 45-64                                                         9.537e-02
## Age_sexMales 65+                                                           1.218e-01
## EducationHigh School Diploma                                               6.901e-02
## EducationLess than High School Diploma                                     9.515e-02
## EducationSome College                                                      8.608e-02
## EthnicityWhite                                                             1.355e-01
## IncomeLevel>$150k                                                          1.295e-01
## IncomeLevel$100-150k                                                       1.031e-01
## IncomeLevel$20-50k                                                         6.743e-02
## IncomeLevel$50-100k                                                        7.286e-02
## BMI                                                                        4.464e-03
## CESD.10baseline                                                            5.471e-03
## SmokingStatusFormer Smoker                                                 9.214e-02
## SmokingStatusNever Smoked                                                  9.630e-02
## SmokingStatusOccasional Smoker                                             1.889e-01
## RelationshipstatusMarried                                                  8.137e-02
## RelationshipstatusSeparated                                                1.584e-01
## RelationshipstatusSingle                                                   1.098e-01
## RelationshipstatusWidowed                                                  1.085e-01
## LivingstatusAssisted Living                                                3.180e-01
## LivingstatusHouse                                                          7.239e-02
## LivingstatusOther                                                          2.481e-01
## AnxietyYes                                                                 9.547e-02
## MoodDisordYes                                                              6.981e-02
## Chronicconditions                                                          1.099e-02
## Animal_Fluency_Normedbaseline                                              6.461e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     9.874e-02
## timefactor2:Age_sexFemales 65+                                             1.315e-01
## timefactor2:Age_sexMales 45-64                                             1.006e-01
## timefactor2:Age_sexMales 65+                                               1.294e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.561e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.317e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.553e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.673e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.408e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.669e-01
##                                                                                   df
## (Intercept)                                                                1.084e+04
## timefactor2                                                                1.025e+04
## PandemicFU2 data collected before COVID-19                                 1.719e+04
## Age_sexFemales 65+                                                         1.661e+04
## Age_sexMales 45-64                                                         1.696e+04
## Age_sexMales 65+                                                           1.693e+04
## EducationHigh School Diploma                                               1.034e+04
## EducationLess than High School Diploma                                     1.042e+04
## EducationSome College                                                      1.027e+04
## EthnicityWhite                                                             1.029e+04
## IncomeLevel>$150k                                                          1.032e+04
## IncomeLevel$100-150k                                                       1.030e+04
## IncomeLevel$20-50k                                                         1.033e+04
## IncomeLevel$50-100k                                                        1.032e+04
## BMI                                                                        1.027e+04
## CESD.10baseline                                                            1.035e+04
## SmokingStatusFormer Smoker                                                 1.038e+04
## SmokingStatusNever Smoked                                                  1.037e+04
## SmokingStatusOccasional Smoker                                             1.029e+04
## RelationshipstatusMarried                                                  1.036e+04
## RelationshipstatusSeparated                                                1.038e+04
## RelationshipstatusSingle                                                   1.034e+04
## RelationshipstatusWidowed                                                  1.032e+04
## LivingstatusAssisted Living                                                1.029e+04
## LivingstatusHouse                                                          1.033e+04
## LivingstatusOther                                                          1.019e+04
## AnxietyYes                                                                 1.033e+04
## MoodDisordYes                                                              1.034e+04
## Chronicconditions                                                          1.031e+04
## Animal_Fluency_Normedbaseline                                              1.031e+04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.017e+04
## timefactor2:Age_sexFemales 65+                                             1.023e+04
## timefactor2:Age_sexMales 45-64                                             1.022e+04
## timefactor2:Age_sexMales 65+                                               1.025e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.715e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.722e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.718e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.017e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.014e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.017e+04
##                                                                           t value
## (Intercept)                                                                17.671
## timefactor2                                                                 3.601
## PandemicFU2 data collected before COVID-19                                  3.034
## Age_sexFemales 65+                                                         -2.599
## Age_sexMales 45-64                                                          0.682
## Age_sexMales 65+                                                           -2.423
## EducationHigh School Diploma                                                3.156
## EducationLess than High School Diploma                                      0.611
## EducationSome College                                                       2.455
## EthnicityWhite                                                              4.338
## IncomeLevel>$150k                                                           1.193
## IncomeLevel$100-150k                                                        2.299
## IncomeLevel$20-50k                                                         -0.178
## IncomeLevel$50-100k                                                         2.624
## BMI                                                                        -0.758
## CESD.10baseline                                                            -2.377
## SmokingStatusFormer Smoker                                                  0.844
## SmokingStatusNever Smoked                                                   0.742
## SmokingStatusOccasional Smoker                                             -0.328
## RelationshipstatusMarried                                                   1.016
## RelationshipstatusSeparated                                                 1.857
## RelationshipstatusSingle                                                    0.689
## RelationshipstatusWidowed                                                  -0.381
## LivingstatusAssisted Living                                                -0.126
## LivingstatusHouse                                                           3.150
## LivingstatusOther                                                          -0.125
## AnxietyYes                                                                  0.644
## MoodDisordYes                                                               1.373
## Chronicconditions                                                          -2.672
## Animal_Fluency_Normedbaseline                                              86.036
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.157
## timefactor2:Age_sexFemales 65+                                             -2.897
## timefactor2:Age_sexMales 45-64                                             -2.115
## timefactor2:Age_sexMales 65+                                               -4.075
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.423
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -2.562
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.820
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.272
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.425
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     2.141
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.000318
## PandemicFU2 data collected before COVID-19                                0.002416
## Age_sexFemales 65+                                                        0.009350
## Age_sexMales 45-64                                                        0.494945
## Age_sexMales 65+                                                          0.015404
## EducationHigh School Diploma                                              0.001605
## EducationLess than High School Diploma                                    0.541516
## EducationSome College                                                     0.014096
## EthnicityWhite                                                            1.45e-05
## IncomeLevel>$150k                                                         0.233001
## IncomeLevel$100-150k                                                      0.021548
## IncomeLevel$20-50k                                                        0.858357
## IncomeLevel$50-100k                                                       0.008703
## BMI                                                                       0.448517
## CESD.10baseline                                                           0.017464
## SmokingStatusFormer Smoker                                                0.398715
## SmokingStatusNever Smoked                                                 0.457912
## SmokingStatusOccasional Smoker                                            0.742889
## RelationshipstatusMarried                                                 0.309445
## RelationshipstatusSeparated                                               0.063361
## RelationshipstatusSingle                                                  0.491057
## RelationshipstatusWidowed                                                 0.703125
## LivingstatusAssisted Living                                               0.899690
## LivingstatusHouse                                                         0.001639
## LivingstatusOther                                                         0.900724
## AnxietyYes                                                                0.519710
## MoodDisordYes                                                             0.169660
## Chronicconditions                                                         0.007562
## Animal_Fluency_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.247282
## timefactor2:Age_sexFemales 65+                                            0.003773
## timefactor2:Age_sexMales 45-64                                            0.034453
## timefactor2:Age_sexMales 65+                                              4.64e-05
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.672629
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.010415
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.412012
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.785574
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.154255
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.032337
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ** 
## Age_sexFemales 65+                                                        ** 
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                          *  
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                       
## EducationSome College                                                     *  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                      *  
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ** 
## BMI                                                                          
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                               .  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ** 
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         ** 
## Animal_Fluency_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            ** 
## timefactor2:Age_sexMales 45-64                                            *  
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             *  
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelAnimals_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF DenDF   F value    Pr(>F)
## timefactor                        1.1     1.1     1 10161    0.2802  0.596560
## Pandemic                         68.2    68.2     1 10346   17.6669 2.654e-05
## Age_sex                         290.6    96.9     3 10344   25.0872 3.636e-16
## Education                        54.6    18.2     3 10347    4.7164  0.002721
## Ethnicity                        72.6    72.6     1 10290   18.8151 1.454e-05
## IncomeLevel                      70.0    17.5     4 10315    4.5353  0.001166
## BMI                               2.2     2.2     1 10274    0.5744  0.448517
## CESD.10baseline                  21.8    21.8     1 10351    5.6510  0.017464
## SmokingStatus                     5.0     1.7     3 10318    0.4349  0.727998
## Relationshipstatus               20.7     5.2     4 10346    1.3374  0.253362
## Livingstatus                     43.2    14.4     3 10265    3.7272  0.010811
## Anxiety                           1.6     1.6     1 10334    0.4145  0.519710
## MoodDisord                        7.3     7.3     1 10337    1.8862  0.169660
## Chronicconditions                27.6    27.6     1 10306    7.1372  0.007562
## Animal_Fluency_Normedbaseline 28580.1 28580.1     1 10308 7402.2611 < 2.2e-16
## timefactor:Pandemic               1.5     1.5     1 10161    0.3813  0.536933
## timefactor:Age_sex              107.6    35.9     3 10158    9.2928 3.921e-06
## Pandemic:Age_sex                 32.7    10.9     3 10346    2.8232  0.037292
## timefactor:Pandemic:Age_sex      21.1     7.0     3 10158    1.8212  0.140912
##                                  
## timefactor                       
## Pandemic                      ***
## Age_sex                       ***
## Education                     ** 
## Ethnicity                     ***
## IncomeLevel                   ** 
## BMI                              
## CESD.10baseline               *  
## SmokingStatus                    
## Relationshipstatus               
## Livingstatus                  *  
## Anxiety                          
## MoodDisord                       
## Chronicconditions             ** 
## Animal_Fluency_Normedbaseline ***
## timefactor:Pandemic              
## timefactor:Age_sex            ***
## Pandemic:Age_sex              *  
## timefactor:Pandemic:Age_sex      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

6.4.2) Estimated marginal means

Significantly lower animal fluency for males and females 65+

lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.94182 0.1571540 11565.78 10.63377
##  FU2 data collected before COVID-19 11.22163 0.1522728 11167.93 10.92315
##  upper.CL
##  11.24987
##  11.52011
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  11.21946 0.1575160 11635.47 10.91070
##  FU2 data collected before COVID-19 11.38502 0.1522720 11167.75 11.08654
##  upper.CL
##  11.52822
##  11.68350
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.61142 0.1720851 12338.96 10.27410
##  FU2 data collected before COVID-19 10.95718 0.1625537 11691.72 10.63855
##  upper.CL
##  10.94873
##  11.27581
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.50797 0.1731142 12518.78 10.16864
##  FU2 data collected before COVID-19 10.78501 0.1627453 11727.44 10.46600
##  upper.CL
##  10.84730
##  11.10402
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  11.00691 0.1510969 11256.97 10.71073
##  FU2 data collected before COVID-19 10.94938 0.1573710 11589.07 10.64091
##  upper.CL
##  11.30308
##  11.25785
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  11.07176 0.1513134 11300.19 10.77516
##  FU2 data collected before COVID-19 11.10056 0.1572686 11569.37 10.79229
##  upper.CL
##  11.36836
##  11.40884
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.64668 0.1722541 12220.68 10.30904
##  FU2 data collected before COVID-19 10.79906 0.1625518 11792.25 10.48043
##  upper.CL
##  10.98433
##  11.11769
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE       df lower.CL
##  FU2 data collected after COVID-19  10.39719 0.1735233 12442.89 10.05706
##  FU2 data collected before COVID-19 10.79255 0.1625739 11796.59 10.47388
##  upper.CL
##  10.73733
##  11.11122
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.2798116 0.09222059 17195.04  -3.034  0.0024
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.1655659 0.09298700 17371.68  -1.781  0.0750
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.3457658 0.12610811 17113.21  -2.742  0.0061
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.2770368 0.12781135 17400.99  -2.168  0.0302
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##   0.0575267 0.09418996 17216.69   0.611  0.5414
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.0287995 0.09433809 17250.51  -0.305  0.7602
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.1523813 0.12513048 17174.64  -1.218  0.2233
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.3953577 0.12680302 17456.73  -3.118  0.0018
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.2798116 0.09222059 17195.04 -0.4605733 -0.09904982
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.1655659 0.09298700 17371.68 -0.3478298  0.01669796
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.3457658 0.12610811 17113.21 -0.5929506 -0.09858097
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.2770368 0.12781135 17400.99 -0.5275599 -0.02651376
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##   0.0575267 0.09418996 17216.69 -0.1270952  0.24214860
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.0287995 0.09433809 17250.51 -0.2137118  0.15611272
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.1523813 0.12513048 17174.64 -0.3976499  0.09288719
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.3953577 0.12680302 17456.73 -0.6439043 -0.14681116
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

6.4.3) Graph of estimated marginal means

Animals_lsmeans_adj11 <- summary(lsmeans(modelAnimals_adj11, ~timefactor|Pandemic|Age_sex))
Animals_lsmeans_adj11$Time<-NA
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

6.4.4) Post-hoc comparisons

There was a significant difference between cohorts for males and females 65+. This post-hoc comparison determines if differences between cohorts at FU1 and FU2 for 65+ males and females are significant.

lsmeans.animals <- lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex)
contrast(lsmeans.animals,list(c2nd, c3rd),by=NULL)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##  structure(c(0, 0, 0, 0, 1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##     estimate        SE       df t.ratio p.value
##   0.24297640 0.1345418 10195.99   1.806  0.0710
##  -0.06872897 0.1350530 10187.46  -0.509  0.6108
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans.animals,list(c2nd, c3rd),by=NULL), parm, level=0.95)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##  structure(c(0, 0, 0, 0, 1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##     estimate        SE       df   lower.CL  upper.CL
##   0.24297640 0.1345418 10195.99 -0.0207520 0.5067048
##  -0.06872897 0.1350530 10187.46 -0.3334594 0.1960015
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

7) Physical Activity Results

7.1) Main effects model adjusted for baseline

7.1.1) Model

modelPASE_2<- lmer(PASE_TOTAL ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long_2)
summary(modelPASE_2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 41379.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6919 -0.5399 -0.0547  0.5147  4.6443 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1120     33.47   
##  Residual             2268     47.62   
## Number of obs: 3793, groups:  ID, 3084
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             227.04329   14.48019
## timefactor2                                             -19.57979    2.64778
## PandemicFU2 data collected before COVID-19               -8.30427    2.75447
## Age                                                      -2.28226    0.13554
## SexM                                                     19.08687    2.26456
## EducationHigh School Diploma                            -10.28018    3.35721
## EducationLess than High School Diploma                   -2.60637    5.33767
## EducationSome College                                     5.82881    3.88524
## EthnicityWhite                                           15.65811    6.50262
## IncomeLevel>$150k                                        10.13841    5.72612
## IncomeLevel$100-150k                                     13.35465    4.63478
## IncomeLevel$20-50k                                        6.55322    3.32002
## IncomeLevel$50-100k                                      10.32450    3.48386
## BMI                                                      -0.44277    0.23659
## CESD.10baseline                                           0.17110    0.26176
## SmokingStatusFormer Smoker                               -2.90890    4.82660
## SmokingStatusNever Smoked                                -0.02248    4.98545
## SmokingStatusOccasional Smoker                           -7.94910    8.41014
## RelationshipstatusMarried                                -3.59607    3.63034
## RelationshipstatusSeparated                               2.93953    7.20676
## RelationshipstatusSingle                                 -6.34248    5.06510
## RelationshipstatusWidowed                                -0.74498    5.15533
## LivingstatusAssisted Living                             -13.91674   14.31955
## LivingstatusHouse                                        13.00541    3.33789
## LivingstatusOther                                        34.09675   13.60157
## AnxietyYes                                               -2.17396    4.50546
## MoodDisordYes                                            -9.86520    3.18407
## Chronicconditions                                        -1.60979    0.52194
## PASE_TOTALbaseline                                        0.36667    0.01506
## timefactor2:PandemicFU2 data collected before COVID-19   15.51764    3.52774
##                                                                df t value
## (Intercept)                                            2954.43057  15.680
## timefactor2                                            2275.73813  -7.395
## PandemicFU2 data collected before COVID-19             3750.05357  -3.015
## Age                                                    2936.56481 -16.838
## SexM                                                   2908.58872   8.429
## EducationHigh School Diploma                           2969.33237  -3.062
## EducationLess than High School Diploma                 2949.45310  -0.488
## EducationSome College                                  2890.33885   1.500
## EthnicityWhite                                         3166.30903   2.408
## IncomeLevel>$150k                                      2905.39647   1.771
## IncomeLevel$100-150k                                   2902.75839   2.881
## IncomeLevel$20-50k                                     2829.44662   1.974
## IncomeLevel$50-100k                                    2840.93470   2.964
## BMI                                                    2952.58643  -1.871
## CESD.10baseline                                        2907.41624   0.654
## SmokingStatusFormer Smoker                             2912.14471  -0.603
## SmokingStatusNever Smoked                              2910.19409  -0.005
## SmokingStatusOccasional Smoker                         2814.23613  -0.945
## RelationshipstatusMarried                              2953.60642  -0.991
## RelationshipstatusSeparated                            2818.72500   0.408
## RelationshipstatusSingle                               2850.65384  -1.252
## RelationshipstatusWidowed                              2905.49242  -0.145
## LivingstatusAssisted Living                            3009.03622  -0.972
## LivingstatusHouse                                      2927.99248   3.896
## LivingstatusOther                                      3066.72319   2.507
## AnxietyYes                                             2935.07572  -0.483
## MoodDisordYes                                          2943.60783  -3.098
## Chronicconditions                                      2856.90903  -3.084
## PASE_TOTALbaseline                                     2899.10049  24.341
## timefactor2:PandemicFU2 data collected before COVID-19 2199.00680   4.399
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.98e-13 ***
## PandemicFU2 data collected before COVID-19              0.00259 ** 
## Age                                                     < 2e-16 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                            0.00222 ** 
## EducationLess than High School Diploma                  0.62538    
## EducationSome College                                   0.13366    
## EthnicityWhite                                          0.01610 *  
## IncomeLevel>$150k                                       0.07674 .  
## IncomeLevel$100-150k                                    0.00399 ** 
## IncomeLevel$20-50k                                      0.04850 *  
## IncomeLevel$50-100k                                     0.00307 ** 
## BMI                                                     0.06138 .  
## CESD.10baseline                                         0.51340    
## SmokingStatusFormer Smoker                              0.54677    
## SmokingStatusNever Smoked                               0.99640    
## SmokingStatusOccasional Smoker                          0.34465    
## RelationshipstatusMarried                               0.32198    
## RelationshipstatusSeparated                             0.68339    
## RelationshipstatusSingle                                0.21060    
## RelationshipstatusWidowed                               0.88511    
## LivingstatusAssisted Living                             0.33119    
## LivingstatusHouse                                      9.99e-05 ***
## LivingstatusOther                                       0.01223 *  
## AnxietyYes                                              0.62947    
## MoodDisordYes                                           0.00196 ** 
## Chronicconditions                                       0.00206 ** 
## PASE_TOTALbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 1.14e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelPASE_2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                      Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## timefactor           101726  101726     1 2197.8  44.8562 2.684e-11 ***
## Pandemic                159     159     1 2897.0   0.0701 0.7911985    
## Age                  642968  642968     1 2936.6 283.5180 < 2.2e-16 ***
## Sex                  161106  161106     1 2908.6  71.0398 < 2.2e-16 ***
## Education             29766    9922     3 2936.2   4.3751 0.0044290 ** 
## Ethnicity             13150   13150     1 3166.3   5.7983 0.0160983 *  
## IncomeLevel           25226    6307     4 2902.4   2.7809 0.0253960 *  
## BMI                    7943    7943     1 2952.6   3.5023 0.0613809 .  
## CESD.10baseline         969     969     1 2907.4   0.4272 0.5134007    
## SmokingStatus          5932    1977     3 2853.5   0.8720 0.4548324    
## Relationshipstatus     6543    1636     4 2848.8   0.7212 0.5773315    
## Livingstatus          47512   15837     3 3005.3   6.9835 0.0001114 ***
## Anxiety                 528     528     1 2935.1   0.2328 0.6294745    
## MoodDisord            21770   21770     1 2943.6   9.5995 0.0019647 ** 
## Chronicconditions     21573   21573     1 2856.9   9.5125 0.0020603 ** 
## PASE_TOTALbaseline  1343657 1343657     1 2899.1 592.4885 < 2.2e-16 ***
## timefactor:Pandemic   43880   43880     1 2199.0  19.3490 1.141e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

7.1.2) Estimated marginal means

lsmeans(modelPASE_2, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean       SE      df lower.CL upper.CL
##  FU2 data collected after COVID-19  159.7396 7.177553 3208.94 145.6665 173.8126
##  FU2 data collected before COVID-19 151.4353 7.142574 3214.64 137.4309 165.4398
## 
## timefactor = 2:
##  Pandemic                             lsmean       SE      df lower.CL upper.CL
##  FU2 data collected after COVID-19  140.1598 7.146889 3191.36 126.1468 154.1728
##  FU2 data collected before COVID-19 147.3732 7.100724 3188.11 133.4507 161.2956
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_2, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df t.ratio p.value
##   8.304269 2.755413 3751.52   3.014  0.0026
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df t.ratio p.value
##  -7.213373 2.669659 3761.29  -2.702  0.0069
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelPASE_2, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df   lower.CL  upper.CL
##   8.304269 2.755413 3751.52   2.902015 13.706523
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df   lower.CL  upper.CL
##  -7.213373 2.669659 3761.29 -12.447492 -1.979254
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

7.1.3) Graph of estimated marginal means

PASE_lsmeans_2 <- summary(lsmeans(modelPASE_2, ~timefactor|Pandemic))
PASE_lsmeans_2$Time<-NA
PASE_lsmeans_2$Time[PASE_lsmeans_2$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_2$Time[PASE_lsmeans_2$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_2, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

7.1.4) Post-hoc comparisons

Test whether differences between cohorts at FU1 and FU2 are significant

lsmeans.PASE <- lsmeans(modelPASE_2, ~Pandemic|timefactor)
contrast(lsmeans.PASE,list(c1st),by=NULL)
##  contrast                                     estimate       SE      df t.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -15.51764 3.529829 2307.99  -4.396
##  p.value
##   <.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger

7.2) Age and Sex Interaction Model

7.2.1) Model

modelPASE_4<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long_2)
summary(modelPASE_4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 41443.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5900 -0.5394 -0.0518  0.4975  4.3962 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1281     35.79   
##  Residual             2248     47.41   
## Number of obs: 3793, groups:  ID, 3084
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                 93.33664
## timefactor2                                                                -25.70659
## PandemicFU2 data collected before COVID-19                                 -13.80217
## Age_sexFemales 65+                                                         -35.79615
## Age_sexMales 45-64                                                          13.17052
## Age_sexMales 65+                                                           -17.99018
## EducationHigh School Diploma                                               -11.03172
## EducationLess than High School Diploma                                      -6.09233
## EducationSome College                                                        3.85447
## EthnicityWhite                                                              12.53759
## IncomeLevel>$150k                                                           11.95503
## IncomeLevel$100-150k                                                        15.37351
## IncomeLevel$20-50k                                                           5.44354
## IncomeLevel$50-100k                                                         10.93393
## BMI                                                                         -0.30116
## CESD.10baseline                                                              0.33349
## SmokingStatusFormer Smoker                                                  -4.52178
## SmokingStatusNever Smoked                                                   -0.97084
## SmokingStatusOccasional Smoker                                              -6.13003
## RelationshipstatusMarried                                                   -2.29172
## RelationshipstatusSeparated                                                  5.86833
## RelationshipstatusSingle                                                    -3.53788
## RelationshipstatusWidowed                                                   -8.94497
## LivingstatusAssisted Living                                                -21.55437
## LivingstatusHouse                                                           15.13757
## LivingstatusOther                                                           35.77027
## AnxietyYes                                                                  -0.18008
## MoodDisordYes                                                               -8.86110
## Chronicconditions                                                           -2.46867
## PASE_TOTALbaseline                                                           0.41899
## timefactor2:PandemicFU2 data collected before COVID-19                      21.17509
## timefactor2:Age_sexFemales 65+                                              12.89167
## timefactor2:Age_sexMales 45-64                                              12.00009
## timefactor2:Age_sexMales 65+                                                -5.32152
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               14.33558
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                6.24467
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 -1.79579
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -13.94747
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -16.63398
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     15.90578
##                                                                           Std. Error
## (Intercept)                                                                 11.92745
## timefactor2                                                                  4.63003
## PandemicFU2 data collected before COVID-19                                   4.66193
## Age_sexFemales 65+                                                           6.86117
## Age_sexMales 45-64                                                           4.83736
## Age_sexMales 65+                                                             6.17899
## EducationHigh School Diploma                                                 3.44383
## EducationLess than High School Diploma                                       5.46166
## EducationSome College                                                        3.98186
## EthnicityWhite                                                               6.65003
## IncomeLevel>$150k                                                            5.86836
## IncomeLevel$100-150k                                                         4.74613
## IncomeLevel$20-50k                                                           3.40474
## IncomeLevel$50-100k                                                          3.57438
## BMI                                                                          0.24216
## CESD.10baseline                                                              0.26816
## SmokingStatusFormer Smoker                                                   4.94836
## SmokingStatusNever Smoked                                                    5.11375
## SmokingStatusOccasional Smoker                                               8.62339
## RelationshipstatusMarried                                                    3.74714
## RelationshipstatusSeparated                                                  7.39034
## RelationshipstatusSingle                                                     5.20164
## RelationshipstatusWidowed                                                    5.26013
## LivingstatusAssisted Living                                                 14.67116
## LivingstatusHouse                                                            3.41900
## LivingstatusOther                                                           13.94851
## AnxietyYes                                                                   4.61549
## MoodDisordYes                                                                3.26584
## Chronicconditions                                                            0.53330
## PASE_TOTALbaseline                                                           0.01477
## timefactor2:PandemicFU2 data collected before COVID-19                       5.90005
## timefactor2:Age_sexFemales 65+                                               8.85680
## timefactor2:Age_sexMales 45-64                                               6.27872
## timefactor2:Age_sexMales 65+                                                 8.33972
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                8.46202
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                6.89888
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  8.17141
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   10.99223
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    8.75135
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     10.76307
##                                                                                   df
## (Intercept)                                                               3120.20879
## timefactor2                                                               2051.15112
## PandemicFU2 data collected before COVID-19                                3722.23633
## Age_sexFemales 65+                                                        3752.97886
## Age_sexMales 45-64                                                        3751.25218
## Age_sexMales 65+                                                          3752.94868
## EducationHigh School Diploma                                              2985.93780
## EducationLess than High School Diploma                                    2964.93494
## EducationSome College                                                     2911.26795
## EthnicityWhite                                                            3160.35277
## IncomeLevel>$150k                                                         2926.59990
## IncomeLevel$100-150k                                                      2920.70539
## IncomeLevel$20-50k                                                        2851.09864
## IncomeLevel$50-100k                                                       2859.91393
## BMI                                                                       2968.34592
## CESD.10baseline                                                           2925.64447
## SmokingStatusFormer Smoker                                                2930.08505
## SmokingStatusNever Smoked                                                 2927.48640
## SmokingStatusOccasional Smoker                                            2840.80522
## RelationshipstatusMarried                                                 2974.45173
## RelationshipstatusSeparated                                               2844.75947
## RelationshipstatusSingle                                                  2874.59836
## RelationshipstatusWidowed                                                 2923.73677
## LivingstatusAssisted Living                                               3021.54585
## LivingstatusHouse                                                         2944.42575
## LivingstatusOther                                                         3077.51156
## AnxietyYes                                                                2951.85121
## MoodDisordYes                                                             2962.52608
## Chronicconditions                                                         2881.45429
## PASE_TOTALbaseline                                                        2928.31196
## timefactor2:PandemicFU2 data collected before COVID-19                    1987.84379
## timefactor2:Age_sexFemales 65+                                            2134.11859
## timefactor2:Age_sexMales 45-64                                            2182.80706
## timefactor2:Age_sexMales 65+                                              2278.93539
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             3737.95537
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             3725.37940
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               3741.78771
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2087.59997
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2109.76182
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   2269.82735
##                                                                           t value
## (Intercept)                                                                 7.825
## timefactor2                                                                -5.552
## PandemicFU2 data collected before COVID-19                                 -2.961
## Age_sexFemales 65+                                                         -5.217
## Age_sexMales 45-64                                                          2.723
## Age_sexMales 65+                                                           -2.912
## EducationHigh School Diploma                                               -3.203
## EducationLess than High School Diploma                                     -1.115
## EducationSome College                                                       0.968
## EthnicityWhite                                                              1.885
## IncomeLevel>$150k                                                           2.037
## IncomeLevel$100-150k                                                        3.239
## IncomeLevel$20-50k                                                          1.599
## IncomeLevel$50-100k                                                         3.059
## BMI                                                                        -1.244
## CESD.10baseline                                                             1.244
## SmokingStatusFormer Smoker                                                 -0.914
## SmokingStatusNever Smoked                                                  -0.190
## SmokingStatusOccasional Smoker                                             -0.711
## RelationshipstatusMarried                                                  -0.612
## RelationshipstatusSeparated                                                 0.794
## RelationshipstatusSingle                                                   -0.680
## RelationshipstatusWidowed                                                  -1.701
## LivingstatusAssisted Living                                                -1.469
## LivingstatusHouse                                                           4.427
## LivingstatusOther                                                           2.564
## AnxietyYes                                                                 -0.039
## MoodDisordYes                                                              -2.713
## Chronicconditions                                                          -4.629
## PASE_TOTALbaseline                                                         28.375
## timefactor2:PandemicFU2 data collected before COVID-19                      3.589
## timefactor2:Age_sexFemales 65+                                              1.456
## timefactor2:Age_sexMales 45-64                                              1.911
## timefactor2:Age_sexMales 65+                                               -0.638
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.694
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.905
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.220
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -1.269
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.901
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.478
##                                                                           Pr(>|t|)
## (Intercept)                                                               6.88e-15
## timefactor2                                                               3.19e-08
## PandemicFU2 data collected before COVID-19                                 0.00309
## Age_sexFemales 65+                                                        1.91e-07
## Age_sexMales 45-64                                                         0.00651
## Age_sexMales 65+                                                           0.00362
## EducationHigh School Diploma                                               0.00137
## EducationLess than High School Diploma                                     0.26474
## EducationSome College                                                      0.33312
## EthnicityWhite                                                             0.05948
## IncomeLevel>$150k                                                          0.04172
## IncomeLevel$100-150k                                                       0.00121
## IncomeLevel$20-50k                                                         0.10997
## IncomeLevel$50-100k                                                        0.00224
## BMI                                                                        0.21373
## CESD.10baseline                                                            0.21374
## SmokingStatusFormer Smoker                                                 0.36090
## SmokingStatusNever Smoked                                                  0.84944
## SmokingStatusOccasional Smoker                                             0.47723
## RelationshipstatusMarried                                                  0.54085
## RelationshipstatusSeparated                                                0.42723
## RelationshipstatusSingle                                                   0.49647
## RelationshipstatusWidowed                                                  0.08914
## LivingstatusAssisted Living                                                0.14189
## LivingstatusHouse                                                         9.88e-06
## LivingstatusOther                                                          0.01038
## AnxietyYes                                                                 0.96888
## MoodDisordYes                                                              0.00670
## Chronicconditions                                                         3.84e-06
## PASE_TOTALbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.00034
## timefactor2:Age_sexFemales 65+                                             0.14566
## timefactor2:Age_sexMales 45-64                                             0.05611
## timefactor2:Age_sexMales 65+                                               0.52348
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.09033
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.36543
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.82607
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.20464
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.05747
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.13960
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ** 
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ** 
## Age_sexMales 65+                                                          ** 
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                            .  
## IncomeLevel>$150k                                                         *  
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ** 
## BMI                                                                          
## CESD.10baseline                                                              
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                 .  
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         *  
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                         ***
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             .  
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 .  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelPASE_4)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## timefactor                    85569   85569     1 2203.4  38.0727 8.087e-10 ***
## Pandemic                         54      54     1 2942.6   0.0242  0.876390    
## Age_sex                      358861  119620     3 2941.1  53.2236 < 2.2e-16 ***
## Education                     29146    9715     3 2953.8   4.3227  0.004764 ** 
## Ethnicity                      7989    7989     1 3160.3   3.5545  0.059475 .  
## IncomeLevel                   33205    8301     4 2921.4   3.6935  0.005268 ** 
## BMI                            3476    3476     1 2968.3   1.5466  0.213732    
## CESD.10baseline                3476    3476     1 2925.6   1.5466  0.213739    
## SmokingStatus                  6921    2307     3 2875.8   1.0264  0.379731    
## Relationshipstatus            10753    2688     4 2870.1   1.1961  0.310384    
## Livingstatus                  61960   20653     3 3018.5   9.1895 4.742e-06 ***
## Anxiety                           3       3     1 2951.8   0.0015  0.968881    
## MoodDisord                    16546   16546     1 2962.5   7.3618  0.006701 ** 
## Chronicconditions             48160   48160     1 2881.4  21.4284 3.835e-06 ***
## PASE_TOTALbaseline          1809573 1809573     1 2928.3 805.1474 < 2.2e-16 ***
## timefactor:Pandemic           45087   45087     1 2205.2  20.0609 7.884e-06 ***
## timefactor:Age_sex             3129    1043     3 2186.6   0.4640  0.707412    
## Pandemic:Age_sex               6950    2317     3 2928.8   1.0308  0.377750    
## timefactor:Pandemic:Age_sex   23076    7692     3 2189.6   3.4225  0.016587 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

7.2.2) Estimated marginal means

Significant differences for females 45-64 and 65+ and significant differences males 65+

lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  164.3075 7.931349 3323.35 148.75665
##  FU2 data collected before COVID-19 150.5053 7.704861 3324.44 135.39855
##  upper.CL
##  179.8583
##  165.6121
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  138.6009 7.955978 3336.64 123.00179
##  FU2 data collected before COVID-19 145.9738 7.569606 3248.00 131.13211
##  upper.CL
##  154.2000
##  160.8155
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  128.5113 9.114301 3519.14 110.64147
##  FU2 data collected before COVID-19 129.0447 8.441698 3465.46 112.49353
##  upper.CL
##  146.3812
##  145.5959
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  115.6964 9.128252 3519.48  97.79919
##  FU2 data collected before COVID-19 123.4574 8.169905 3347.79 107.43892
##  upper.CL
##  133.5936
##  139.4759
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  177.4780 7.790232 3310.17 162.20383
##  FU2 data collected before COVID-19 169.9205 8.094849 3397.42 154.04923
##  upper.CL
##  192.7522
##  185.7918
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  163.7715 7.772753 3302.16 148.53158
##  FU2 data collected before COVID-19 160.7551 7.975077 3345.14 145.11859
##  upper.CL
##  179.0114
##  176.3916
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  146.3173 8.747337 3430.51 129.16678
##  FU2 data collected before COVID-19 130.7193 8.428004 3435.55 114.19493
##  upper.CL
##  163.4678
##  147.2437
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE      df  lower.CL
##  FU2 data collected after COVID-19  115.2892 9.127440 3543.08  97.39362
##  FU2 data collected before COVID-19 136.7721 8.226797 3362.42 120.64206
##  upper.CL
##  133.1848
##  152.9021
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   13.802165 4.663612 3725.15   2.960  0.0031
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   -7.372924 4.452537 3748.32  -1.656  0.0978
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   -0.533419 7.072622 3744.51  -0.075  0.9399
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   -7.761034 7.183009 3739.30  -1.080  0.2800
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##    7.557492 5.101771 3728.70   1.481  0.1386
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##    3.016380 4.798867 3751.39   0.629  0.5297
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   15.597954 6.727868 3748.14   2.318  0.0205
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##  -21.482911 6.936701 3739.82  -3.097  0.0020
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##   13.802165 4.663612 3725.15   4.65868 22.945648
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##   -7.372924 4.452537 3748.32 -16.10255  1.356708
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##   -0.533419 7.072622 3744.51 -14.39999 13.333148
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##   -7.761034 7.183009 3739.30 -21.84403  6.321963
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##    7.557492 5.101771 3728.70  -2.44504 17.560026
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##    3.016380 4.798867 3751.39  -6.39226 12.425022
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##   15.597954 6.727868 3748.14   2.40731 28.788593
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df  lower.CL  upper.CL
##  -21.482911 6.936701 3739.82 -35.08300 -7.882825
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

7.2.3) Graph of estimated marginal means

PASE_lsmeans_4 <- summary(lsmeans(modelPASE_4, ~timefactor|Pandemic|Age_sex))
PASE_lsmeans_4$Time<-NA
PASE_lsmeans_4$Time[PASE_lsmeans_4$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_4$Time[PASE_lsmeans_4$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_4, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

7.2.4) Post-hoc

lsmeans.PASE1 <- lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex)
contrast(lsmeans.PASE1,list(c2nd,c3rd,c4th),by=NULL)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##  structure(c(0, 0, 0, 0, 1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##  structure(c(1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##  estimate       SE      df t.ratio p.value
##  37.08086 9.016610 2481.62   4.113  <.0001
##   7.22761 9.277665 2222.13   0.779  0.4360
##  21.17509 5.903681 2081.27   3.587  0.0003
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans.PASE1,list(c2nd,c3rd,c4th),by=NULL), parm, level=0.95)
##  contrast                                                                    
##  structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1), dim = c(16L, 
##  structure(c(0, 0, 0, 0, 1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##  structure(c(1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(16L, 
##  estimate       SE      df   lower.CL upper.CL
##  37.08086 9.016610 2481.62  19.400010 54.76172
##   7.22761 9.277665 2222.13 -10.966183 25.42141
##  21.17509 5.903681 2081.27   9.597354 32.75282
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

8) Sedentary behaviour Results

8.1) Full model adjusted for baseline

Create binary variable for 4+ hrs/day of SB

Tracking.data_long_2$SB.binary <- as.factor(ifelse(Tracking.data_long_2$PASE_Sit==10, 1, 0))
Tracking.data_long_2$SBbaseline.binary <- as.factor(ifelse(Tracking.data_long_2$PASE_Sitbaseline==10, 1, 0))

8.1.1) Model

sit1 <- glmer(
  SB.binary ~ timefactor*Pandemic + Age + Sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 6.09947 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
summary(sit1)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic + Age + Sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  25491.0  25736.6 -12714.5  25429.0    20362 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9806 -0.6527 -0.4617  0.7984  2.0561 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.7907   0.8892  
## Number of obs: 20393, groups:  ID, 10230
## 
## Fixed effects:
##                                                         Estimate Std. Error
## (Intercept)                                            -2.348903   0.236246
## timefactor2                                             0.481092   0.048627
## PandemicFU2 data collected before COVID-19              0.081017   0.050449
## Age                                                     0.015428   0.002263
## SexM                                                   -0.134353   0.041323
## SBbaseline.binary1                                      1.147014   0.040784
## EducationHigh School Diploma                           -0.004636   0.058051
## EducationLess than High School Diploma                  0.097418   0.079337
## EducationSome College                                  -0.083815   0.072726
## EthnicityWhite                                         -0.130210   0.113829
## IncomeLevel>$150k                                      -0.184708   0.110608
## IncomeLevel$100-150k                                   -0.011710   0.086806
## IncomeLevel$20-50k                                     -0.141811   0.056574
## IncomeLevel$50-100k                                    -0.218146   0.061474
## BMI                                                     0.039803   0.003841
## CESD.10baseline                                         0.012359   0.004615
## SmokingStatusFormer Smoker                             -0.300883   0.077152
## SmokingStatusNever Smoked                              -0.323008   0.080713
## SmokingStatusOccasional Smoker                         -0.338149   0.160344
## RelationshipstatusMarried                              -0.173398   0.067979
## RelationshipstatusSeparated                             0.098122   0.132808
## RelationshipstatusSingle                                0.067729   0.092096
## RelationshipstatusWidowed                               0.010810   0.090877
## LivingstatusAssisted Living                             0.296195   0.268864
## LivingstatusHouse                                      -0.232848   0.060525
## LivingstatusOther                                      -0.497680   0.215550
## AnxietyYes                                             -0.011266   0.080091
## MoodDisordYes                                           0.104958   0.058511
## Chronicconditions                                       0.037485   0.009378
## timefactor2:PandemicFU2 data collected before COVID-19 -0.439137   0.065361
##                                                        z value Pr(>|z|)    
## (Intercept)                                             -9.943  < 2e-16 ***
## timefactor2                                              9.893  < 2e-16 ***
## PandemicFU2 data collected before COVID-19               1.606 0.108294    
## Age                                                      6.819 9.17e-12 ***
## SexM                                                    -3.251 0.001149 ** 
## SBbaseline.binary1                                      28.124  < 2e-16 ***
## EducationHigh School Diploma                            -0.080 0.936343    
## EducationLess than High School Diploma                   1.228 0.219486    
## EducationSome College                                   -1.152 0.249120    
## EthnicityWhite                                          -1.144 0.252661    
## IncomeLevel>$150k                                       -1.670 0.094935 .  
## IncomeLevel$100-150k                                    -0.135 0.892688    
## IncomeLevel$20-50k                                      -2.507 0.012189 *  
## IncomeLevel$50-100k                                     -3.549 0.000387 ***
## BMI                                                     10.363  < 2e-16 ***
## CESD.10baseline                                          2.678 0.007407 ** 
## SmokingStatusFormer Smoker                              -3.900 9.62e-05 ***
## SmokingStatusNever Smoked                               -4.002 6.28e-05 ***
## SmokingStatusOccasional Smoker                          -2.109 0.034954 *  
## RelationshipstatusMarried                               -2.551 0.010749 *  
## RelationshipstatusSeparated                              0.739 0.460013    
## RelationshipstatusSingle                                 0.735 0.462088    
## RelationshipstatusWidowed                                0.119 0.905315    
## LivingstatusAssisted Living                              1.102 0.270613    
## LivingstatusHouse                                       -3.847 0.000120 ***
## LivingstatusOther                                       -2.309 0.020950 *  
## AnxietyYes                                              -0.141 0.888136    
## MoodDisordYes                                            1.794 0.072843 .  
## Chronicconditions                                        3.997 6.42e-05 ***
## timefactor2:PandemicFU2 data collected before COVID-19  -6.719 1.83e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 6.09947 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sit1)
## Analysis of Variance Table
##                     npar  Sum Sq Mean Sq   F value
## timefactor             1   42.38   42.38   42.3822
## Pandemic               1    0.82    0.82    0.8173
## Age                    1  157.08  157.08  157.0774
## Sex                    1   34.35   34.35   34.3516
## SBbaseline.binary      1 1110.10 1110.10 1110.0952
## Education              3   13.13    4.38    4.3757
## Ethnicity              1    1.27    1.27    1.2739
## IncomeLevel            4   25.18    6.29    6.2950
## BMI                    1  146.78  146.78  146.7836
## CESD.10baseline        1   25.98   25.98   25.9779
## SmokingStatus          3   24.88    8.29    8.2932
## Relationshipstatus     4   38.19    9.55    9.5486
## Livingstatus           3   22.79    7.60    7.5958
## Anxiety                1    0.44    0.44    0.4387
## MoodDisord             1    4.92    4.92    4.9198
## Chronicconditions      1   17.14   17.14   17.1401
## timefactor:Pandemic    1   49.83   49.83   49.8273

8.1.2) Predicted probabilities

ggpredict(sit1, c("timefactor", "Pandemic"))
## # Predicted probabilities of SB.binary
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.46 | [0.38, 0.54]
## 2          |      0.58 | [0.50, 0.65]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.48 | [0.40, 0.56]
## 2          |      0.49 | [0.41, 0.57]
## 
## Adjusted for:
## *                Age =                    60.00
## *                Sex =                        F
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.52
## *    CESD.10baseline =                     4.87
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.76
## *                 ID =     0 (population-level)

8.1.3) Estimated Mean Differences Between Pre- and Post-pandemic Cohorts at FU1 and FU2

Calculating estimated mean differences between cohorts

#Create data frame
sit.test <- as.data.frame(ggpredict(sit1, c("timefactor", "Pandemic")))

#Determine standard errors
sit.test$standard.error <- (sit.test$predicted - sit.test$conf.low)/1.96

#Calculating estimated mean differences between cohorts at FU1
mean.diff.1<-(subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.1<-(sqrt(((subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.1<-mean.diff.1 + se.1*1.96
LL.1<-mean.diff.1 - se.1*1.96


#Calculating estimated mean differences between cohorts at FU2
mean.diff.2<-(subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.2<-(sqrt(((subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.2<-mean.diff.2 + se.2*1.96
LL.2<-mean.diff.2 - se.2*1.96


#Calculating z-scores for differences
z.1<- (subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
     (subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))
     
z.2<- (subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))


#p-values for z-scores
p.1<-2*pnorm(z.1, mean = 0, sd = 1, lower.tail = TRUE)
p.2<-2*pnorm(z.2, mean = 0, sd = 1, lower.tail = FALSE)

Estimated mean differences between cohorts at FU1

mean.diff.1 #mean difference
## [1] -0.02017452
LL.1 #Lower 95% CI
## [1] -0.09679234
UL.1 #Upper 95% CI
## [1] 0.05644329
z.1 # z-score
## [1] -0.5160949
p.1 #p-value
## [1] 0.6057881

Estimated mean differences between cohorts at FU2

mean.diff.2 #mean difference
## [1] 0.088871
LL.2 #Lower 95% CI
## [1] 0.01132347
UL.2 #Upper 95% CI
## [1] 0.1664185
z.2 # z-score
## [1] 2.246199
p.2 #p-value
## [1] 0.02469128

8.1.4) Graph of predicted probabilities

ggpredict(sit1, c("timefactor", "Pandemic")) %>% plot()

8.1.5) Post-hoc analysis

Calculating between-cohort changes from FU1 to FU2

#mean difference
mean.diff.1a<-((subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$predicted))

mean.diff.1b<-((subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$predicted - 
         subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$predicted))

mean.change<- mean.diff.1a - mean.diff.1b

#standard error
se.change<-(sqrt(((subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$standard.error^2 + 
            (subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$standard.error)^2 + 
            (subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$standard.error)^2 ))/4))

#confidence interval
UL.change<-mean.change + se.change*1.96
LL.change<-mean.change - se.change*1.96

#t-score
t.change<-mean.change/se.change

#degrees of freedom
df <- length(Tracking.data_long_2$SB.binary)/2 - 2

#p-value
p.change <- pt(t.change, df)

Estimated between-cohort changes from FU1 to FU2

mean.change #Between-cohort changes from FU1 to FU2
## [1] -0.1090455
LL.change #LL 95% CI
## [1] -0.186593
UL.change #UL 95% CI
## [1] -0.03149799
p.change #p-value
## [1] 0.002929366

8.2) Age and Sex Interaction Model

8.2.1) Model

sit2 <- glmer(
  SB.binary ~ timefactor*Pandemic*Age_sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.884797 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
summary(sit2)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic * Age_sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  25497.3  25822.1 -12707.6  25415.3    20352 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9822 -0.6494 -0.4575  0.7975  2.0630 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.8118   0.901   
## Number of obs: 20393, groups:  ID, 10230
## 
## Fixed effects:
##                                                                            Estimate
## (Intercept)                                                               -1.620686
## timefactor2                                                                0.580652
## PandemicFU2 data collected before COVID-19                                 0.057124
## Age_sexFemales 65+                                                         0.398934
## Age_sexMales 45-64                                                        -0.146265
## Age_sexMales 65+                                                           0.303371
## SBbaseline.binary1                                                         1.150707
## EducationHigh School Diploma                                              -0.005042
## EducationLess than High School Diploma                                     0.096709
## EducationSome College                                                     -0.073105
## EthnicityWhite                                                            -0.054078
## IncomeLevel>$150k                                                         -0.206336
## IncomeLevel$100-150k                                                      -0.032560
## IncomeLevel$20-50k                                                        -0.162937
## IncomeLevel$50-100k                                                       -0.243375
## BMI                                                                        0.039126
## CESD.10baseline                                                            0.011728
## SmokingStatusFormer Smoker                                                -0.290040
## SmokingStatusNever Smoked                                                 -0.315386
## SmokingStatusOccasional Smoker                                            -0.327601
## RelationshipstatusMarried                                                 -0.176033
## RelationshipstatusSeparated                                                0.061149
## RelationshipstatusSingle                                                   0.068453
## RelationshipstatusWidowed                                                  0.044016
## LivingstatusAssisted Living                                                0.309546
## LivingstatusHouse                                                         -0.239899
## LivingstatusOther                                                         -0.451692
## AnxietyYes                                                                -0.039052
## MoodDisordYes                                                              0.112731
## Chronicconditions                                                          0.042309
## timefactor2:PandemicFU2 data collected before COVID-19                    -0.342007
## timefactor2:Age_sexFemales 65+                                            -0.141938
## timefactor2:Age_sexMales 45-64                                            -0.060952
## timefactor2:Age_sexMales 65+                                              -0.124570
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -0.015392
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.104367
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.094448
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.173610
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.037797
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -0.387304
##                                                                           Std. Error
## (Intercept)                                                                 0.198762
## timefactor2                                                                 0.090756
## PandemicFU2 data collected before COVID-19                                  0.089637
## Age_sexFemales 65+                                                          0.120383
## Age_sexMales 45-64                                                          0.093659
## Age_sexMales 65+                                                            0.116712
## SBbaseline.binary1                                                          0.040980
## EducationHigh School Diploma                                                0.058380
## EducationLess than High School Diploma                                      0.079825
## EducationSome College                                                       0.073104
## EthnicityWhite                                                              0.114565
## IncomeLevel>$150k                                                           0.111039
## IncomeLevel$100-150k                                                        0.086968
## IncomeLevel$20-50k                                                          0.056951
## IncomeLevel$50-100k                                                         0.061771
## BMI                                                                         0.003850
## CESD.10baseline                                                             0.004632
## SmokingStatusFormer Smoker                                                  0.077453
## SmokingStatusNever Smoked                                                   0.081139
## SmokingStatusOccasional Smoker                                              0.161099
## RelationshipstatusMarried                                                   0.068431
## RelationshipstatusSeparated                                                 0.133442
## RelationshipstatusSingle                                                    0.092551
## RelationshipstatusWidowed                                                   0.090937
## LivingstatusAssisted Living                                                 0.269851
## LivingstatusHouse                                                           0.060734
## LivingstatusOther                                                           0.216237
## AnxietyYes                                                                  0.080499
## MoodDisordYes                                                               0.058878
## Chronicconditions                                                           0.009322
## timefactor2:PandemicFU2 data collected before COVID-19                      0.116220
## timefactor2:Age_sexFemales 65+                                              0.152290
## timefactor2:Age_sexMales 45-64                                              0.119074
## timefactor2:Age_sexMales 65+                                                0.149668
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.148398
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.128492
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.148056
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.193903
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.166757
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     0.193614
##                                                                           z value
## (Intercept)                                                                -8.154
## timefactor2                                                                 6.398
## PandemicFU2 data collected before COVID-19                                  0.637
## Age_sexFemales 65+                                                          3.314
## Age_sexMales 45-64                                                         -1.562
## Age_sexMales 65+                                                            2.599
## SBbaseline.binary1                                                         28.080
## EducationHigh School Diploma                                               -0.086
## EducationLess than High School Diploma                                      1.212
## EducationSome College                                                      -1.000
## EthnicityWhite                                                             -0.472
## IncomeLevel>$150k                                                          -1.858
## IncomeLevel$100-150k                                                       -0.374
## IncomeLevel$20-50k                                                         -2.861
## IncomeLevel$50-100k                                                        -3.940
## BMI                                                                        10.162
## CESD.10baseline                                                             2.532
## SmokingStatusFormer Smoker                                                 -3.745
## SmokingStatusNever Smoked                                                  -3.887
## SmokingStatusOccasional Smoker                                             -2.034
## RelationshipstatusMarried                                                  -2.572
## RelationshipstatusSeparated                                                 0.458
## RelationshipstatusSingle                                                    0.740
## RelationshipstatusWidowed                                                   0.484
## LivingstatusAssisted Living                                                 1.147
## LivingstatusHouse                                                          -3.950
## LivingstatusOther                                                          -2.089
## AnxietyYes                                                                 -0.485
## MoodDisordYes                                                               1.915
## Chronicconditions                                                           4.539
## timefactor2:PandemicFU2 data collected before COVID-19                     -2.943
## timefactor2:Age_sexFemales 65+                                             -0.932
## timefactor2:Age_sexMales 45-64                                             -0.512
## timefactor2:Age_sexMales 65+                                               -0.832
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -0.104
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.812
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.638
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -0.895
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.227
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -2.000
##                                                                           Pr(>|z|)
## (Intercept)                                                               3.52e-16
## timefactor2                                                               1.58e-10
## PandemicFU2 data collected before COVID-19                                0.523944
## Age_sexFemales 65+                                                        0.000920
## Age_sexMales 45-64                                                        0.118364
## Age_sexMales 65+                                                          0.009341
## SBbaseline.binary1                                                         < 2e-16
## EducationHigh School Diploma                                              0.931183
## EducationLess than High School Diploma                                    0.225699
## EducationSome College                                                     0.317305
## EthnicityWhite                                                            0.636904
## IncomeLevel>$150k                                                         0.063136
## IncomeLevel$100-150k                                                      0.708113
## IncomeLevel$20-50k                                                        0.004223
## IncomeLevel$50-100k                                                       8.15e-05
## BMI                                                                        < 2e-16
## CESD.10baseline                                                           0.011349
## SmokingStatusFormer Smoker                                                0.000181
## SmokingStatusNever Smoked                                                 0.000102
## SmokingStatusOccasional Smoker                                            0.041998
## RelationshipstatusMarried                                                 0.010100
## RelationshipstatusSeparated                                               0.646774
## RelationshipstatusSingle                                                  0.459530
## RelationshipstatusWidowed                                                 0.628367
## LivingstatusAssisted Living                                               0.251340
## LivingstatusHouse                                                         7.82e-05
## LivingstatusOther                                                         0.036719
## AnxietyYes                                                                0.627587
## MoodDisordYes                                                             0.055537
## Chronicconditions                                                         5.66e-06
## timefactor2:PandemicFU2 data collected before COVID-19                    0.003253
## timefactor2:Age_sexFemales 65+                                            0.351326
## timefactor2:Age_sexMales 45-64                                            0.608733
## timefactor2:Age_sexMales 65+                                              0.405232
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.917392
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.416652
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.523526
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.370603
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.820689
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.045458
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                          ** 
## SBbaseline.binary1                                                        ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                         .  
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                        ** 
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ***
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                ***
## SmokingStatusNever Smoked                                                 ***
## SmokingStatusOccasional Smoker                                            *  
## RelationshipstatusMarried                                                 *  
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         *  
## AnxietyYes                                                                   
## MoodDisordYes                                                             .  
## Chronicconditions                                                         ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 0.884797 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sit2)
## Analysis of Variance Table
##                             npar  Sum Sq Mean Sq   F value
## timefactor                     1   47.21   47.21   47.2072
## Pandemic                       1    0.63    0.63    0.6335
## Age_sex                        3  165.87   55.29   55.2895
## SBbaseline.binary              1 1111.16 1111.16 1111.1597
## Education                      3   15.41    5.14    5.1366
## Ethnicity                      1    0.14    0.14    0.1429
## IncomeLevel                    4   29.44    7.36    7.3606
## BMI                            1  142.34  142.34  142.3416
## CESD.10baseline                1   24.29   24.29   24.2938
## SmokingStatus                  3   22.62    7.54    7.5397
## Relationshipstatus             4   40.84   10.21   10.2109
## Livingstatus                   3   24.32    8.11    8.1067
## Anxiety                        1    0.10    0.10    0.0995
## MoodDisord                     1    5.46    5.46    5.4621
## Chronicconditions              1   22.25   22.25   22.2471
## timefactor:Pandemic            1   55.38   55.38   55.3783
## timefactor:Age_sex             3   18.28    6.09    6.0918
## Pandemic:Age_sex               3    4.40    1.47    1.4676
## timefactor:Pandemic:Age_sex    3    5.12    1.71    1.7064

8.2.2) Predicted probabilities

ggpredict(sit2, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of SB.binary
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.41 | [0.33, 0.49]
## FU2 data collected before COVID-19 |      0.42 | [0.35, 0.50]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.55 | [0.47, 0.63]
## FU2 data collected before COVID-19 |      0.48 | [0.40, 0.56]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.51 | [0.42, 0.60]
## FU2 data collected before COVID-19 |      0.52 | [0.43, 0.60]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.61 | [0.53, 0.70]
## FU2 data collected before COVID-19 |      0.50 | [0.41, 0.58]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.37 | [0.30, 0.45]
## FU2 data collected before COVID-19 |      0.41 | [0.33, 0.50]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.50 | [0.42, 0.58]
## FU2 data collected before COVID-19 |      0.45 | [0.36, 0.53]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.48 | [0.39, 0.57]
## FU2 data collected before COVID-19 |      0.52 | [0.44, 0.61]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.60 | [0.51, 0.68]
## FU2 data collected before COVID-19 |      0.45 | [0.37, 0.54]
## 
## Adjusted for:
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.52
## *    CESD.10baseline =                     4.87
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.76
## *                 ID =     0 (population-level)
sit.test2 <- as.data.frame(ggpredict(sit2, c("timefactor", "Pandemic", "Age_sex")))

sit.test2$standard.error <- (sit.test2$predicted - sit.test2$conf.low)/1.96

8.2.3) Estimated mean differences between pre- and post-pandemic cohorts by age and sex

Calculating mean differences and 95% CIs for each age/sex group

######### Females 45-64 years #########

#Calculating means and standard errors for females 45-64 years
mean.diff.Females.Young.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.1<-mean.diff.Females.Young.1 + se.Females.Young.1*1.96
LL.Females.Young.1<-mean.diff.Females.Young.1 - se.Females.Young.1*1.96

mean.diff.Females.Young.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.2<-mean.diff.Females.Young.2 + se.Females.Young.2*1.96
LL.Females.Young.2<-mean.diff.Females.Young.2 - se.Females.Young.2*1.96

#z-scores for females 45-64 years
z.Females.Young.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))
     
z.Females.Young.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

#p-values for females 45-64 years
p.Females.Young.1<-2*pnorm(z.Females.Young.1, mean = 0, sd = 1, lower.tail = TRUE)
p.Females.Young.2<-2*pnorm(z.Females.Young.2, mean = 0, sd = 1, lower.tail = FALSE)


########### Females 65+ years ###########

#Calculating means and standard errors for females  65+ years
mean.diff.Females.Old.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.1<-mean.diff.Females.Old.1 + se.Females.Old.1*1.96
LL.Females.Old.1<-mean.diff.Females.Old.1 - se.Females.Old.1*1.96

mean.diff.Females.Old.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.2<-mean.diff.Females.Old.2 + se.Females.Old.2*1.96
LL.Females.Old.2<-mean.diff.Females.Old.2 - se.Females.Old.2*1.96

#z-scores for females 65+ years
z.Females.Old.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))
     
z.Females.Old.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

#p-values for females 65+ years
p.Females.Old.1<-2*pnorm(z.Females.Old.1, mean = 0, sd = 1, lower.tail = TRUE)
p.Females.Old.2<-2*pnorm(z.Females.Old.2, mean = 0, sd = 1, lower.tail = FALSE)



########## Males 45-64 years ###########

#Calculating means and standard errors for males 45-64 years
mean.diff.Males.Young.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.1<-mean.diff.Males.Young.1 + se.Males.Young.1*1.96
LL.Males.Young.1<-mean.diff.Males.Young.1 - se.Males.Young.1*1.96

mean.diff.Males.Young.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.2<-mean.diff.Males.Young.2 + se.Males.Young.2*1.96
LL.Males.Young.2<-mean.diff.Males.Young.2 - se.Males.Young.2*1.96

#z-scores for males 45-64 years
z.Males.Young.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))
     
z.Males.Young.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

#p-values for males 45-64 years
p.Males.Young.1<-2*pnorm(z.Males.Young.1, mean = 0, sd = 1, lower.tail = TRUE)
p.Males.Young.2<-2*pnorm(z.Males.Young.2, mean = 0, sd = 1, lower.tail = FALSE)


########## Males 65+ years ###########

#Calculating means and standard errors for males 65+ years
mean.diff.Males.Old.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.1<-mean.diff.Males.Old.1 + se.Males.Old.1*1.96
LL.Males.Old.1<-mean.diff.Males.Old.1 - se.Males.Old.1*1.96

mean.diff.Males.Old.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.2<-mean.diff.Males.Old.2 + se.Males.Old.2*1.96
LL.Males.Old.2<-mean.diff.Males.Old.2 - se.Males.Old.2*1.96

#z-scores for males 65+ years
z.Males.Old.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))
     
z.Males.Old.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

#p-values for males 65+ years
p.Males.Old.1<-2*pnorm(z.Males.Old.1, mean = 0, sd = 1, lower.tail = TRUE)
p.Males.Old.2<-2*pnorm(z.Males.Old.2, mean = 0, sd = 1, lower.tail = FALSE)

Mean differences and 95% CIs for females 45-64 years at FU1

mean.diff.Females.Young.1 #Estimated mean difference
## [1] -0.01387204
LL.Females.Young.1 #LL CI
## [1] -0.09123671
UL.Females.Young.1 #UL CI
## [1] 0.06349263
z.Females.Young.1 #z-score
## [1] -0.351442
p.Females.Young.1 #p-value
## [1] 0.7252568

Mean differences and 95% CIs for females 45-64 years at FU2

mean.diff.Females.Young.2 #Estimated mean difference
## [1] 0.07101829
LL.Females.Young.2 #LL CI
## [1] -0.01060118
UL.Females.Young.2 #UL CI
## [1] 0.1526378
z.Females.Young.2 #z-score
## [1] 1.705425
p.Females.Young.2 #p-value
## [1] 0.08811527

Mean differences and 95% CIs for females 65+ years at FU1

mean.diff.Females.Old.1 #Estimated mean difference
## [1] -0.01042611
LL.Females.Old.1 #LL CI
## [1] -0.09700985
UL.Females.Old.1 #UL CI
## [1] 0.07615763
z.Females.Old.1 #z-score
## [1] -0.2360162
p.Females.Old.1 #p-value
## [1] 0.8134201

Mean differences and 95% CIs for females 65+ years at FU2 (significant)

mean.diff.Females.Old.2 #Estimated mean difference
## [1] 0.1163847
LL.Females.Old.2 #LL CI
## [1] 0.03057621
UL.Females.Old.2 #UL CI
## [1] 0.2021932
z.Females.Old.2 #z-score
## [1] 2.658408
p.Females.Old.2 #p-value
## [1] 0.007851067

Mean differences and 95% CIs for males 45-64 years at FU1

mean.diff.Males.Young.1 #Estimated mean difference
## [1] -0.03849999
LL.Males.Young.1 #LL CI
## [1] -0.1151633
UL.Males.Young.1 #UL CI
## [1] 0.03816328
z.Males.Young.1 #z-score
## [1] -0.9843042
p.Males.Young.1 #p-value
## [1] 0.324966

Mean differences and 95% CIs for males 45-64 years at FU2

mean.diff.Males.Young.2 #Estimated mean difference
## [1] 0.05437307
LL.Males.Young.2 #LL CI
## [1] -0.02740747
UL.Males.Young.2 #UL CI
## [1] 0.1361536
z.Males.Young.2 #z-score
## [1] 1.303137
p.Males.Young.2 #p-value
## [1] 0.1925281

Mean differences and 95% CIs for males 65+ years at FU1

mean.diff.Males.Old.1 #Estimated mean difference
## [1] -0.0378739
LL.Males.Old.1 #LL CI
## [1] -0.1253354
UL.Males.Old.1 #UL CI
## [1] 0.04958757
z.Males.Old.1 #z-score
## [1] -0.8487491
p.Males.Old.1 #p-value
## [1] 0.3960209

Mean differences and 95% CIs for males 65+ years at FU2

mean.diff.Males.Old.2 #Estimated mean difference
## [1] 0.1430834
LL.Males.Old.2 #LL CI
## [1] 0.0566674
UL.Males.Old.2 #UL CI
## [1] 0.2294994
z.Males.Old.2 #z-score
## [1] 3.245273
p.Males.Old.2 #p-value
## [1] 0.001173382

8.2.4) Graph of predicted probabilities

ggpredict(sit2, c("timefactor","Pandemic","Age_sex")) %>% plot()

8.2.5) Post-hoc comparisons

There were significant differences between Pre- and Post-pandemic cohorts at FU2 for females 65+ and for males 65+ . We now examine whether there significant changes from FU1 to FU2 in the probability of sitting 4+ hours/day between Pre- and Post-pandemic cohorts for females 65+ and males 65+.

Calculating between-cohort changes from FU1 to FU2 for females 65+ and males 65+

####### Females 65+ ###########

#mean difference
mean.diff.Females.Old.1a<-((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted))

mean.diff.Females.Old.1b<-((subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted))

mean.Females.Old.change<- mean.diff.Females.Old.1a - mean.diff.Females.Old.1b

#standard error
se.change.Females.Old <-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error^2 + 
            (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error)^2 + 
            (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error)^2 ))/4))

#confidence interval
UL.Females.Old.change<-mean.Females.Old.change + se.change.Females.Old*1.96
LL.Females.Old.change<-mean.Females.Old.change - se.change.Females.Old*1.96

#t-score
t.Females.Old.change<-mean.change/se.change.Females.Old

#degrees of freedom
df.Females.Old <- nrow(Tracking.data_long_2[Tracking.data_long_2$Age_sex == "Females 65+" & !is.na(Tracking.data_long_2$SB.binary), ])/2 - 2


#p-value
p.change.Females.Old <- pt(t.Females.Old.change, df.Females.Old)

####### Males 65+ ###########

#mean difference
mean.diff.Males.Old.1a<-((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted))

mean.diff.Males.Old.1b<-((subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted))

mean.Males.Old.change<- mean.diff.Males.Old.1a - mean.diff.Males.Old.1b

#standard error
se.change.Males.Old <-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error^2 + 
            (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error)^2 + 
            (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error)^2 ))/4))

#confidence interval
UL.Males.Old.change<-mean.Males.Old.change + se.change.Males.Old*1.96
LL.Males.Old.change<-mean.Males.Old.change - se.change.Males.Old*1.96

#t-score
t.Males.Old.change<-mean.Males.Old.change/se.change.Males.Old

#degrees of freedom
df.Males.Old <- nrow(Tracking.data_long_2[Tracking.data_long_2$Age_sex == "Males 65+" & !is.na(Tracking.data_long_2$SB.binary), ])/2 - 2


#p-value
p.change.Males.Old <- pt(t.Males.Old.change, df.Males.Old)

Estimated between-cohort changes from FU1 to FU2 for females 65+

mean.Females.Old.change #Between-cohort changes from FU1 to FU2
## [1] -0.1268108
LL.Females.Old.change #LL 95% CI
## [1] -0.2126193
UL.Females.Old.change #UL 95% CI
## [1] -0.04100232
p.change.Females.Old #p-value
## [1] 0.00641108

Estimated between-cohort changes from FU1 to FU2 for males 65+

mean.Males.Old.change #Between-cohort changes from FU1 to FU2
## [1] -0.1809573
LL.Males.Old.change #LL 95% CI
## [1] -0.2673733
UL.Males.Old.change #UL 95% CI
## [1] -0.0945413
p.change.Males.Old #p-value
## [1] 2.109284e-05

9) Sleep Results

9.1) Main effects model adjusted for baseline

9.1.1) Model

sleep1 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic + Age + Sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 1.36972 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
summary(sleep1)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## RSTLS_Sleep ~ timefactor * Pandemic + Age + Sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  24480.7  24726.9 -12209.3  24418.7    20731 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1468 -0.5765 -0.4261  0.8298  2.1750 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.8005   0.8947  
## Number of obs: 20762, groups:  ID, 10407
## 
## Fixed effects:
##                                                         Estimate Std. Error
## (Intercept)                                            -1.101766   0.240638
## timefactor2                                            -0.184424   0.049089
## PandemicFU2 data collected before COVID-19             -0.007853   0.050754
## Age                                                    -0.011876   0.002320
## SexM                                                   -0.209542   0.042217
## RSTLS_Sleepbaseline                                     0.913048   0.044699
## EducationHigh School Diploma                            0.139834   0.058837
## EducationLess than High School Diploma                  0.224461   0.080637
## EducationSome College                                   0.148781   0.073649
## EthnicityWhite                                          0.159890   0.119647
## IncomeLevel>$150k                                      -0.062121   0.113232
## IncomeLevel$100-150k                                    0.013985   0.088940
## IncomeLevel$20-50k                                     -0.044483   0.057356
## IncomeLevel$50-100k                                     0.009227   0.062277
## BMI                                                     0.001927   0.003810
## CESD.10baseline                                         0.063722   0.005144
## SmokingStatusFormer Smoker                              0.051360   0.079391
## SmokingStatusNever Smoked                              -0.066645   0.083063
## SmokingStatusOccasional Smoker                         -0.105624   0.162376
## RelationshipstatusMarried                              -0.059996   0.069278
## RelationshipstatusSeparated                            -0.383550   0.138357
## RelationshipstatusSingle                               -0.216066   0.094755
## RelationshipstatusWidowed                              -0.147863   0.093431
## LivingstatusAssisted Living                             0.206873   0.271411
## LivingstatusHouse                                       0.140978   0.063074
## LivingstatusOther                                      -0.055422   0.217175
## AnxietyYes                                             -0.145088   0.080983
## MoodDisordYes                                          -0.032438   0.059146
## Chronicconditions                                       0.086628   0.009520
## timefactor2:PandemicFU2 data collected before COVID-19 -0.011079   0.066700
##                                                        z value Pr(>|z|)    
## (Intercept)                                             -4.579 4.68e-06 ***
## timefactor2                                             -3.757 0.000172 ***
## PandemicFU2 data collected before COVID-19              -0.155 0.877031    
## Age                                                     -5.118 3.08e-07 ***
## SexM                                                    -4.963 6.93e-07 ***
## RSTLS_Sleepbaseline                                     20.427  < 2e-16 ***
## EducationHigh School Diploma                             2.377 0.017471 *  
## EducationLess than High School Diploma                   2.784 0.005376 ** 
## EducationSome College                                    2.020 0.043368 *  
## EthnicityWhite                                           1.336 0.181437    
## IncomeLevel>$150k                                       -0.549 0.583270    
## IncomeLevel$100-150k                                     0.157 0.875055    
## IncomeLevel$20-50k                                      -0.776 0.438006    
## IncomeLevel$50-100k                                      0.148 0.882216    
## BMI                                                      0.506 0.613004    
## CESD.10baseline                                         12.387  < 2e-16 ***
## SmokingStatusFormer Smoker                               0.647 0.517687    
## SmokingStatusNever Smoked                               -0.802 0.422357    
## SmokingStatusOccasional Smoker                          -0.650 0.515376    
## RelationshipstatusMarried                               -0.866 0.386476    
## RelationshipstatusSeparated                             -2.772 0.005568 ** 
## RelationshipstatusSingle                                -2.280 0.022592 *  
## RelationshipstatusWidowed                               -1.583 0.113518    
## LivingstatusAssisted Living                              0.762 0.445933    
## LivingstatusHouse                                        2.235 0.025411 *  
## LivingstatusOther                                       -0.255 0.798573    
## AnxietyYes                                              -1.792 0.073201 .  
## MoodDisordYes                                           -0.548 0.583389    
## Chronicconditions                                        9.099  < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19  -0.166 0.868079    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 1.36972 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sleep1)
## Analysis of Variance Table
##                     npar Sum Sq Mean Sq  F value
## timefactor             1  25.91   25.91  25.9052
## Pandemic               1   0.20    0.20   0.1998
## Age                    1  11.97   11.97  11.9731
## Sex                    1  70.89   70.89  70.8865
## RSTLS_Sleepbaseline    1 960.16  960.16 960.1553
## Education              3  23.50    7.83   7.8345
## Ethnicity              1   1.25    1.25   1.2520
## IncomeLevel            4   7.11    1.78   1.7772
## BMI                    1  10.21   10.21  10.2080
## CESD.10baseline        1 200.15  200.15 200.1478
## SmokingStatus          3   9.69    3.23   3.2316
## Relationshipstatus     4  14.95    3.74   3.7387
## Livingstatus           3   5.08    1.69   1.6926
## Anxiety                1   1.30    1.30   1.2971
## MoodDisord             1   0.08    0.08   0.0831
## Chronicconditions      1  89.46   89.46  89.4606
## timefactor:Pandemic    1   0.03    0.03   0.0296

9.1.2) Predicted probabilities

ggpredict(sleep1, c("timefactor", "Pandemic"))
## # Predicted probabilities of RSTLS_Sleep
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.29 | [0.22, 0.36]
## 2          |      0.25 | [0.19, 0.32]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.28 | [0.22, 0.35]
## 2          |      0.25 | [0.19, 0.31]
## 
## Adjusted for:
## *                 Age =                    60.00
## *                 Sex =                        F
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.53
## *     CESD.10baseline =                     4.88
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.76
## *                  ID =     0 (population-level)

9.1.3) Estimated Mean Differences Between Pre- and Post-pandemic Cohorts at FU1 and FU2

Calculating estimated mean differences between cohorts

#Create data frame
sleep.test <- as.data.frame(ggpredict(sleep1, c("timefactor", "Pandemic")))

#Determine standard errors
sleep.test$standard.error <- (sleep.test$predicted - sleep.test$conf.low)/1.96

#Calculating estimated mean differences between cohorts at FU1
mean.diff.1a<-(subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.1a<-(sqrt(((subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.1a<-mean.diff.1a + se.1a*1.96
LL.1a<-mean.diff.1a - se.1a*1.96


#Calculating estimated mean differences between cohorts at FU2
mean.diff.2a<-(subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.2a<-(sqrt(((subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.2a<-mean.diff.2a + se.2a*1.96
LL.2a<-mean.diff.2a - se.2a*1.96


#Calculating z-scores for differences
z.1a<- (subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
     (subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))
     
z.2a<- (subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))


#p-values for z-scores
p.1a<-2*pnorm(z.1a, mean = 0, sd = 1, lower.tail = FALSE)
p.2a<-2*pnorm(z.2a, mean = 0, sd = 1, lower.tail = FALSE)

Estimated mean differences between cohorts at FU1

mean.diff.1a #mean difference
## [1] 0.001602157
LL.1a #Lower 95% CI
## [1] -0.05942352
UL.1a #Upper 95% CI
## [1] 0.06262783
z.1a # z-score
## [1] 0.05145749
p.1a #p-value
## [1] 0.958961

Estimated mean differences between cohorts at FU2

mean.diff.2a #mean difference
## [1] 0.003534739
LL.2a #Lower 95% CI
## [1] -0.0517981
UL.2a #Upper 95% CI
## [1] 0.05886758
z.2a # z-score
## [1] 0.1252075
p.2a #p-value
## [1] 0.9003593

9.1.4) Graph of predicted probabilities

ggpredict(sleep1, c("timefactor", "Pandemic")) %>% plot()

9.1.5) Post-hoc analysis

There are no significant differences between cohorts, and thus no post-hoc testing.

9.2) Age and Sex Interaction Model

9.2.1) Model

sleep2 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic*Age_sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 3.59238 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
summary(sleep2)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: RSTLS_Sleep ~ timefactor * Pandemic * Age_sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  24481.7  24807.3 -12199.9  24399.7    20721 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2534 -0.5763 -0.4240  0.8275  2.3373 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.8078   0.8988  
## Number of obs: 20762, groups:  ID, 10407
## 
## Fixed effects:
##                                                                            Estimate
## (Intercept)                                                               -1.671773
## timefactor2                                                               -0.281688
## PandemicFU2 data collected before COVID-19                                -0.155181
## Age_sexFemales 65+                                                        -0.226663
## Age_sexMales 45-64                                                        -0.510486
## Age_sexMales 65+                                                          -0.477572
## RSTLS_Sleepbaseline                                                        0.911139
## EducationHigh School Diploma                                               0.135689
## EducationLess than High School Diploma                                     0.204534
## EducationSome College                                                      0.132670
## EthnicityWhite                                                             0.131442
## IncomeLevel>$150k                                                          0.014420
## IncomeLevel$100-150k                                                       0.077726
## IncomeLevel$20-50k                                                        -0.022403
## IncomeLevel$50-100k                                                        0.037373
## BMI                                                                        0.003336
## CESD.10baseline                                                            0.064369
## SmokingStatusFormer Smoker                                                 0.031118
## SmokingStatusNever Smoked                                                 -0.077333
## SmokingStatusOccasional Smoker                                            -0.088391
## RelationshipstatusMarried                                                 -0.053105
## RelationshipstatusSeparated                                               -0.359240
## RelationshipstatusSingle                                                  -0.191453
## RelationshipstatusWidowed                                                 -0.167265
## LivingstatusAssisted Living                                                0.229839
## LivingstatusHouse                                                          0.165971
## LivingstatusOther                                                         -0.064622
## AnxietyYes                                                                -0.121646
## MoodDisordYes                                                             -0.026124
## Chronicconditions                                                          0.084981
## timefactor2:PandemicFU2 data collected before COVID-19                     0.107955
## timefactor2:Age_sexFemales 65+                                             0.103330
## timefactor2:Age_sexMales 45-64                                             0.256567
## timefactor2:Age_sexMales 65+                                              -0.159097
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -0.076275
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.365992
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.140755
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.134282
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.374952
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.245235
##                                                                           Std. Error
## (Intercept)                                                                 0.201694
## timefactor2                                                                 0.088815
## PandemicFU2 data collected before COVID-19                                  0.086727
## Age_sexFemales 65+                                                          0.119201
## Age_sexMales 45-64                                                          0.091301
## Age_sexMales 65+                                                            0.117270
## RSTLS_Sleepbaseline                                                         0.044788
## EducationHigh School Diploma                                                0.058972
## EducationLess than High School Diploma                                      0.080867
## EducationSome College                                                       0.073819
## EthnicityWhite                                                              0.119736
## IncomeLevel>$150k                                                           0.113209
## IncomeLevel$100-150k                                                        0.088858
## IncomeLevel$20-50k                                                          0.057582
## IncomeLevel$50-100k                                                         0.062382
## BMI                                                                         0.003808
## CESD.10baseline                                                             0.005150
## SmokingStatusFormer Smoker                                                  0.079473
## SmokingStatusNever Smoked                                                   0.083269
## SmokingStatusOccasional Smoker                                              0.162494
## RelationshipstatusMarried                                                   0.069558
## RelationshipstatusSeparated                                                 0.138577
## RelationshipstatusSingle                                                    0.094911
## RelationshipstatusWidowed                                                   0.093262
## LivingstatusAssisted Living                                                 0.270375
## LivingstatusHouse                                                           0.063166
## LivingstatusOther                                                           0.217816
## AnxietyYes                                                                  0.081036
## MoodDisordYes                                                               0.059317
## Chronicconditions                                                           0.009423
## timefactor2:PandemicFU2 data collected before COVID-19                      0.114861
## timefactor2:Age_sexFemales 65+                                              0.152293
## timefactor2:Age_sexMales 45-64                                              0.118828
## timefactor2:Age_sexMales 65+                                                0.156193
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.148510
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.126508
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.150348
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.196828
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.168221
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     0.201830
##                                                                           z value
## (Intercept)                                                                -8.289
## timefactor2                                                                -3.172
## PandemicFU2 data collected before COVID-19                                 -1.789
## Age_sexFemales 65+                                                         -1.902
## Age_sexMales 45-64                                                         -5.591
## Age_sexMales 65+                                                           -4.072
## RSTLS_Sleepbaseline                                                        20.344
## EducationHigh School Diploma                                                2.301
## EducationLess than High School Diploma                                      2.529
## EducationSome College                                                       1.797
## EthnicityWhite                                                              1.098
## IncomeLevel>$150k                                                           0.127
## IncomeLevel$100-150k                                                        0.875
## IncomeLevel$20-50k                                                         -0.389
## IncomeLevel$50-100k                                                         0.599
## BMI                                                                         0.876
## CESD.10baseline                                                            12.498
## SmokingStatusFormer Smoker                                                  0.392
## SmokingStatusNever Smoked                                                  -0.929
## SmokingStatusOccasional Smoker                                             -0.544
## RelationshipstatusMarried                                                  -0.763
## RelationshipstatusSeparated                                                -2.592
## RelationshipstatusSingle                                                   -2.017
## RelationshipstatusWidowed                                                  -1.794
## LivingstatusAssisted Living                                                 0.850
## LivingstatusHouse                                                           2.628
## LivingstatusOther                                                          -0.297
## AnxietyYes                                                                 -1.501
## MoodDisordYes                                                              -0.440
## Chronicconditions                                                           9.019
## timefactor2:PandemicFU2 data collected before COVID-19                      0.940
## timefactor2:Age_sexFemales 65+                                              0.678
## timefactor2:Age_sexMales 45-64                                              2.159
## timefactor2:Age_sexMales 65+                                               -1.019
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -0.514
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               2.893
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.936
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -0.682
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -2.229
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.215
##                                                                           Pr(>|z|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                0.00152
## PandemicFU2 data collected before COVID-19                                 0.07357
## Age_sexFemales 65+                                                         0.05723
## Age_sexMales 45-64                                                        2.25e-08
## Age_sexMales 65+                                                          4.65e-05
## RSTLS_Sleepbaseline                                                        < 2e-16
## EducationHigh School Diploma                                               0.02140
## EducationLess than High School Diploma                                     0.01143
## EducationSome College                                                      0.07230
## EthnicityWhite                                                             0.27231
## IncomeLevel>$150k                                                          0.89864
## IncomeLevel$100-150k                                                       0.38173
## IncomeLevel$20-50k                                                         0.69724
## IncomeLevel$50-100k                                                        0.54910
## BMI                                                                        0.38112
## CESD.10baseline                                                            < 2e-16
## SmokingStatusFormer Smoker                                                 0.69539
## SmokingStatusNever Smoked                                                  0.35304
## SmokingStatusOccasional Smoker                                             0.58647
## RelationshipstatusMarried                                                  0.44519
## RelationshipstatusSeparated                                                0.00953
## RelationshipstatusSingle                                                   0.04368
## RelationshipstatusWidowed                                                  0.07289
## LivingstatusAssisted Living                                                0.39528
## LivingstatusHouse                                                          0.00860
## LivingstatusOther                                                          0.76671
## AnxietyYes                                                                 0.13332
## MoodDisordYes                                                              0.65964
## Chronicconditions                                                          < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.34728
## timefactor2:Age_sexFemales 65+                                             0.49746
## timefactor2:Age_sexMales 45-64                                             0.03084
## timefactor2:Age_sexMales 65+                                               0.30839
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.60753
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.00382
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.34917
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.49509
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.02582
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.22435
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ** 
## PandemicFU2 data collected before COVID-19                                .  
## Age_sexFemales 65+                                                        .  
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## RSTLS_Sleepbaseline                                                       ***
## EducationHigh School Diploma                                              *  
## EducationLess than High School Diploma                                    *  
## EducationSome College                                                     .  
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                          
## BMI                                                                          
## CESD.10baseline                                                           ***
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                               ** 
## RelationshipstatusSingle                                                  *  
## RelationshipstatusWidowed                                                 .  
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ** 
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            *  
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             ** 
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 *  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 3.59238 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sleep2)
## Analysis of Variance Table
##                             npar Sum Sq Mean Sq  F value
## timefactor                     1  26.02   26.02  26.0210
## Pandemic                       1   0.22    0.22   0.2236
## Age_sex                        3  91.71   30.57  30.5712
## RSTLS_Sleepbaseline            1 950.15  950.15 950.1510
## Education                      3  22.41    7.47   7.4687
## Ethnicity                      1   0.97    0.97   0.9700
## IncomeLevel                    4   4.54    1.14   1.1353
## BMI                            1  11.99   11.99  11.9911
## CESD.10baseline                1 203.24  203.24 203.2397
## SmokingStatus                  3   8.11    2.70   2.7025
## Relationshipstatus             4  13.54    3.39   3.3861
## Livingstatus                   3   6.62    2.21   2.2072
## Anxiety                        1   0.71    0.71   0.7068
## MoodDisord                     1   0.13    0.13   0.1324
## Chronicconditions              1  88.62   88.62  88.6201
## timefactor:Pandemic            1   0.00    0.00   0.0018
## timefactor:Age_sex             3   1.24    0.41   0.4143
## Pandemic:Age_sex               3  13.69    4.56   4.5630
## timefactor:Pandemic:Age_sex    3  10.74    3.58   3.5806

9.2.2) Predicted probabilities

ggpredict(sleep2, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of RSTLS_Sleep
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.32 | [0.25, 0.40]
## FU2 data collected before COVID-19 |      0.29 | [0.23, 0.36]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.27 | [0.20, 0.34]
## FU2 data collected before COVID-19 |      0.26 | [0.20, 0.33]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.28 | [0.21, 0.36]
## FU2 data collected before COVID-19 |      0.23 | [0.18, 0.30]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.24 | [0.18, 0.32]
## FU2 data collected before COVID-19 |      0.20 | [0.15, 0.26]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.17, 0.29]
## FU2 data collected before COVID-19 |      0.26 | [0.20, 0.33]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.17, 0.28]
## FU2 data collected before COVID-19 |      0.21 | [0.16, 0.27]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.23 | [0.17, 0.30]
## FU2 data collected before COVID-19 |      0.23 | [0.17, 0.30]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.16 | [0.12, 0.22]
## FU2 data collected before COVID-19 |      0.21 | [0.16, 0.28]
## 
## Adjusted for:
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.53
## *     CESD.10baseline =                     4.88
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.76
## *                  ID =     0 (population-level)
sleep.test2 <- as.data.frame(ggpredict(sleep2, c("timefactor", "Pandemic", "Age_sex")))

sleep.test2$standard.error <- (sleep.test2$predicted - sleep.test2$conf.low)/1.96

9.2.3) Estimated mean differences between pre- and post-pandemic cohorts by age and sex

Calculating mean differences and 95% CIs for each age/sex group

######### Females 45-64 years #########

#Calculating means and standard errors for females 45-64 years
mean.diff.Females.Young.1a<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                               subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)

se.Females.Young.1a<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                              (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.1a<-mean.diff.Females.Young.1a + se.Females.Young.1a*1.96
LL.Females.Young.1a<-mean.diff.Females.Young.1a - se.Females.Young.1a*1.96

mean.diff.Females.Young.2a<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                               subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)

se.Females.Young.2a<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                              (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.2a<-mean.diff.Females.Young.2a + se.Females.Young.2a*1.96
LL.Females.Young.2a<-mean.diff.Females.Young.2a - se.Females.Young.2a*1.96

#z-scores for females 45-64 years
z.Females.Young.1a <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                         subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                                                                                                                                                   (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

z.Females.Young.2a <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                         subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                                                                                                                                                   (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

#p-values for females 45-64 years
p.Females.Young.1a<-2*pnorm(z.Females.Young.1a, mean = 0, sd = 1, lower.tail = FALSE)
p.Females.Young.2a<-2*pnorm(z.Females.Young.2a, mean = 0, sd = 1, lower.tail = FALSE)


########### Females 65+ years ###########

#Calculating means and standard errors for females  65+ years
mean.diff.Females.Old.1a<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                             subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)

se.Females.Old.1a<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                            (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.1a<-mean.diff.Females.Old.1a + se.Females.Old.1a*1.96
LL.Females.Old.1a<-mean.diff.Females.Old.1a - se.Females.Old.1a*1.96

mean.diff.Females.Old.2a<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                             subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)

se.Females.Old.2a<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                            (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.2a<-mean.diff.Females.Old.2a + se.Females.Old.2a*1.96
LL.Females.Old.2a<-mean.diff.Females.Old.2a - se.Females.Old.2a*1.96

#z-scores for females 65+ years
z.Females.Old.1a <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                       subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                                                                                                                                               (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

z.Females.Old.2a <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                       subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                                                                                                                                               (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

#p-values for females 65+ years
p.Females.Old.1a<-2*pnorm(z.Females.Old.1a, mean = 0, sd = 1, lower.tail = FALSE)
p.Females.Old.2a<-2*pnorm(z.Females.Old.2a, mean = 0, sd = 1, lower.tail = FALSE)



########## Males 45-64 years ###########

#Calculating means and standard errors for males 45-64 years
mean.diff.Males.Young.1a<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                             subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)

se.Males.Young.1a<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                            (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.1a<-mean.diff.Males.Young.1a + se.Males.Young.1a*1.96
LL.Males.Young.1a<-mean.diff.Males.Young.1a - se.Males.Young.1a*1.96

mean.diff.Males.Young.2a<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                             subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)

se.Males.Young.2a<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                            (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.2a<-mean.diff.Males.Young.2a + se.Males.Young.2a*1.96
LL.Males.Young.2a<-mean.diff.Males.Young.2a - se.Males.Young.2a*1.96

#z-scores for males 45-64 years
z.Males.Young.1a <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                       subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                                                                                                                                               (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

z.Males.Young.2a <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                       subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                                                                                                                                               (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

#p-values for males 45-64 years
p.Males.Young.1a<-2*pnorm(z.Males.Young.1a, mean = 0, sd = 1, lower.tail = TRUE)
p.Males.Young.2a<-2*pnorm(z.Males.Young.2a, mean = 0, sd = 1, lower.tail = FALSE)


########## Males 65+ years ###########

#Calculating means and standard errors for males 65+ years
mean.diff.Males.Old.1a<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                           subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)

se.Males.Old.1a<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                          (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.1a<-mean.diff.Males.Old.1a + se.Males.Old.1a*1.96
LL.Males.Old.1a<-mean.diff.Males.Old.1a - se.Males.Old.1a*1.96

mean.diff.Males.Old.2a<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                           subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)

se.Males.Old.2a<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                          (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.2a<-mean.diff.Males.Old.2a + se.Males.Old.2a*1.96
LL.Males.Old.2a<-mean.diff.Males.Old.2a - se.Males.Old.2a*1.96

#z-scores for males 65+ years
z.Males.Old.1a <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                     subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                                                                                                                                           (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

z.Males.Old.2a <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                     subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                                                                                                                                           (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

#p-values for males 65+ years
p.Males.Old.1a<-2*pnorm(z.Males.Old.1a, mean = 0, sd = 1, lower.tail = FALSE)
p.Males.Old.2a<-2*pnorm(z.Males.Old.2a, mean = 0, sd = 1, lower.tail = TRUE)

Mean differences and 95% CIs for females 45-64 years at FU1

mean.diff.Females.Young.1a #Estimated mean difference
## [1] 0.0330407
LL.Females.Young.1a #LL CI
## [1] -0.03371057
UL.Females.Young.1a #UL CI
## [1] 0.09979196
z.Females.Young.1a #z-score
## [1] 0.9701654
p.Females.Young.1a #p-value
## [1] 0.3319641

Mean differences and 95% CIs for females 45-64 years at FU2

mean.diff.Females.Young.2a #Estimated mean difference
## [1] 0.009115943
LL.Females.Young.2a #LL CI
## [1] -0.05066293
UL.Females.Young.2a #UL CI
## [1] 0.06889482
z.Females.Young.2a #z-score
## [1] 0.298889
p.Females.Young.2a #p-value
## [1] 0.7650247

Mean differences and 95% CIs for females 65+ years at FU1

mean.diff.Females.Old.1a #Estimated mean difference
## [1] 0.04386866
LL.Females.Old.1a #LL CI
## [1] -0.01806807
UL.Females.Old.1a #UL CI
## [1] 0.1058054
z.Females.Old.1a #z-score
## [1] 1.388232
p.Females.Old.1a #p-value
## [1] 0.1650663

Mean differences and 95% CIs for females 65+ years at FU2

mean.diff.Females.Old.2a #Estimated mean difference
## [1] 0.04418037
LL.Females.Old.2a #LL CI
## [1] -0.01156697
UL.Females.Old.2a #UL CI
## [1] 0.09992771
z.Females.Old.2a #z-score
## [1] 1.553321
p.Females.Old.2a #p-value
## [1] 0.1203464

Mean differences and 95% CIs for males 45-64 years at FU1

mean.diff.Males.Young.1a #Estimated mean difference
## [1] -0.03871951
LL.Males.Young.1a #LL CI
## [1] -0.09638396
UL.Males.Young.1a #UL CI
## [1] 0.01894493
z.Males.Young.1a #z-score
## [1] -1.316067
p.Males.Young.1a #p-value
## [1] 0.1881517

Mean differences and 95% CIs for males 45-64 years at FU2

mean.diff.Males.Young.2a #Estimated mean difference
## [1] 0.009469295
LL.Males.Young.2a #LL CI
## [1] -0.04297747
UL.Males.Young.2a #UL CI
## [1] 0.06191606
z.Males.Young.2a #z-score
## [1] 0.3538792
p.Males.Young.2a #p-value
## [1] 0.7234294

Mean differences and 95% CIs for males 65+ years at FU1

mean.diff.Males.Old.1a #Estimated mean difference
## [1] 0.002540603
LL.Males.Old.1a #LL CI
## [1] -0.05517548
UL.Males.Old.1a #UL CI
## [1] 0.06025669
z.Males.Old.1a #z-score
## [1] 0.0862772
p.Males.Old.1a #p-value
## [1] 0.9312461

Mean differences and 95% CIs for males 65+ years at FU2 (significant)

mean.diff.Males.Old.2a #Estimated mean difference
## [1] -0.05110064
LL.Males.Old.2a #LL CI
## [1] -0.1004843
UL.Males.Old.2a #UL CI
## [1] -0.001716938
z.Males.Old.2a #z-score
## [1] -2.028144
p.Males.Old.2a #p-value
## [1] 0.04254556

9.2.4) Graph of predicted probabilities

ggpredict(sleep2, c("timefactor","Pandemic","Age_sex")) %>% plot()

9.2.5) Post-hoc comparisons

There were significant differences between Pre- and Post-pandemic cohorts at FU2 for males 65+ . We now examine whether there significant changes from FU1 to FU2 in the probability of sitting 4+ hours/day between Pre- and Post-pandemic cohorts for males 65+.

Calculating between-cohort changes from FU1 to FU2 for males 65+

####### Males 65+ ###########

#mean difference
mean.diff.Males.Old.1c<-((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted))

mean.diff.Males.Old.1d<-((subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted))

mean.Males.Old.change.1<- mean.diff.Males.Old.1c - mean.diff.Males.Old.1d

#standard error
se.change.Males.Old.1 <-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
         (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error^2 + 
            (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error)^2 + 
            (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error)^2 ))/4))

#confidence interval
UL.Males.Old.change.1<-mean.Males.Old.change.1 + se.change.Males.Old.1*1.96
LL.Males.Old.change.1<-mean.Males.Old.change.1 - se.change.Males.Old.1*1.96

#t-score
t.Males.Old.change.1<-mean.Males.Old.change.1/se.change.Males.Old.1

#degrees of freedom
df.Males.Old.1 <- nrow(Tracking.data_long_2[Tracking.data_long_2$Age_sex == "Males 65+" & !is.na(Tracking.data_long_2$RSTLS_Sleep), ])/2 - 2


#p-value
p.change.Males.Old.1 <- pt(t.Males.Old.change.1, df.Males.Old.1)

Estimated between-cohort changes from FU1 to FU2 for males 65+

mean.Males.Old.change.1 #Between-cohort changes from FU1 to FU2
## [1] 0.05364124
LL.Males.Old.change.1 #LL 95% CI
## [1] 0.004257541
UL.Males.Old.change.1 #UL 95% CI
## [1] 0.1030249
p.change.Males.Old.1 #p-value
## [1] 0.9833111

10) Changes in lifestyle behaviours associated with changes in cognition

10.1) Change score development

Develop change scores in cognition from FU1 to FU2

Tracking.Adjusted_Final$RVLT_Immediate_Change<-Tracking.Adjusted_Final$RVLT_Immediate_Normed_1 - Tracking.Adjusted_Final$RVLT_Immediate_Normed_2 # RVLT Immediate; Lower Scores = Declines
Tracking.Adjusted_Final$RVLT_Delayed_Change<-Tracking.Adjusted_Final$RVLT_Delayed_Normed_1 - Tracking.Adjusted_Final$RVLT_Delayed_Normed_2 # RVLT Delayed; Lower Scores = Declines 
Tracking.Adjusted_Final$MAT_Change<-Tracking.Adjusted_Final$MAT_Normed_1 - Tracking.Adjusted_Final$MAT_Normed_2  # MAT; Lower Scores = Declines 
Tracking.Adjusted_Final$Animal_Change<-Tracking.Adjusted_Final$Animal_Fluency_Normed_1 - Tracking.Adjusted_Final$Animal_Fluency_Normed_2 # MAT Scores; Lower Score = Declines

Develop change scores in Physical Activity

Tracking.Adjusted_Final$PASE_Change<-Tracking.Adjusted_Final$PASE_TOTAL_1 - Tracking.Adjusted_Final$PASE_TOTAL_2

Develop change scores in Sedentary Behaviour

Tracking.Adjusted_Final$SB.binary_0 <- as.factor(ifelse(Tracking.Adjusted_Final$PASE_Q1B_0==10, 1, 0))
Tracking.Adjusted_Final$SB.binary_1 <- as.factor(ifelse(Tracking.Adjusted_Final$PASE_Q1B_1==10, 1, 0))
Tracking.Adjusted_Final$SB.binary_2 <- as.factor(ifelse(Tracking.Adjusted_Final$PASE_Q1B_2==10, 1, 0))

Tracking.Adjusted_Final <- Tracking.Adjusted_Final %>%
  mutate(SB_binary_change = ifelse(Tracking.Adjusted_Final$SB.binary_1 == 0 & Tracking.Adjusted_Final$SB.binary_2 == 1, 1, 0))

Develop change scores in sleep

Tracking.Adjusted_Final <- Tracking.Adjusted_Final %>%
  mutate(Sleep_change = ifelse(Tracking.Adjusted_Final$RSTLS_Sleep_1 == 0 & Tracking.Adjusted_Final$RSTLS_Sleep_2 == 1, 1, 0))

Age x Sex groups

Tracking.Adjusted_Final$Age_sex<-NA
Tracking.Adjusted_Final$Age_sex[Tracking.Adjusted_Final$Age_0<=64 & Tracking.Adjusted_Final$Sex_0 == "M"]<-"Males 45-64"
Tracking.Adjusted_Final$Age_sex[Tracking.Adjusted_Final$Age_0<=64 & Tracking.Adjusted_Final$Sex_0 == "F"]<-"Females 45-64"
Tracking.Adjusted_Final$Age_sex[Tracking.Adjusted_Final$Age_0>64 & Tracking.Adjusted_Final$Sex_0 == "M"]<-"Males 65+"
Tracking.Adjusted_Final$Age_sex[Tracking.Adjusted_Final$Age_0>64 & Tracking.Adjusted_Final$Sex_0 == "F"]<-"Females 65+"

10.2) Change score regressions

All regressions are stratified by age group

10.2.1) Changes in physical activity associated with changes in cognition

RVLT Immediate

RVLT_immediate_change_model_PA.1<- lm(RVLT_Immediate_Change~PASE_Change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(RVLT_immediate_change_model_PA.1)
## 
## Call:
## lm(formula = RVLT_Immediate_Change ~ PASE_Change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.3843  -2.0480   0.1728   2.3168  10.1583 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)                    -0.241396   0.363851  -0.663    0.508
## PASE_Change                    -0.003020   0.004974  -0.607    0.544
## Age_sexFemales 65+              0.286761   0.695763   0.412    0.681
## Age_sexMales 45-64             -0.645826   0.502842  -1.284    0.200
## Age_sexMales 65+                0.183342   0.730511   0.251    0.802
## PASE_Change:Age_sexFemales 65+  0.019278   0.014291   1.349    0.178
## PASE_Change:Age_sexMales 45-64  0.006968   0.006767   1.030    0.304
## PASE_Change:Age_sexMales 65+    0.007104   0.013316   0.533    0.594
## 
## Residual standard error: 3.739 on 322 degrees of freedom
##   (4851 observations deleted due to missingness)
## Multiple R-squared:  0.02028,    Adjusted R-squared:  -0.001021 
## F-statistic: 0.952 on 7 and 322 DF,  p-value: 0.4665
anova(RVLT_immediate_change_model_PA.1)
## Analysis of Variance Table
## 
## Response: RVLT_Immediate_Change
##                      Df Sum Sq Mean Sq F value Pr(>F)
## PASE_Change           1    5.8  5.7759  0.4132 0.5208
## Age_sex               3   54.4 18.1434  1.2980 0.2751
## PASE_Change:Age_sex   3   32.9 10.9829  0.7857 0.5026
## Residuals           322 4500.9 13.9781

RVLT Delayed

RVLT_delayed_change_model_PA.1<- lm(RVLT_Delayed_Change~PASE_Change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(RVLT_delayed_change_model_PA.1)
## 
## Call:
## lm(formula = RVLT_Delayed_Change ~ PASE_Change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.0894  -2.2134   0.1358   2.2087  10.1317 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                    -0.839824   0.349281  -2.404   0.0168 *
## PASE_Change                    -0.002105   0.004772  -0.441   0.6595  
## Age_sexFemales 65+              0.495719   0.672882   0.737   0.4618  
## Age_sexMales 45-64             -0.214120   0.482270  -0.444   0.6574  
## Age_sexMales 65+                1.264534   0.712172   1.776   0.0768 .
## PASE_Change:Age_sexFemales 65+  0.007428   0.013664   0.544   0.5871  
## PASE_Change:Age_sexMales 45-64  0.008803   0.006476   1.359   0.1751  
## PASE_Change:Age_sexMales 65+   -0.003506   0.012744  -0.275   0.7834  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.565 on 314 degrees of freedom
##   (4859 observations deleted due to missingness)
## Multiple R-squared:  0.02424,    Adjusted R-squared:  0.002489 
## F-statistic: 1.114 on 7 and 314 DF,  p-value: 0.3536
anova(RVLT_delayed_change_model_PA.1)
## Analysis of Variance Table
## 
## Response: RVLT_Delayed_Change
##                      Df Sum Sq Mean Sq F value Pr(>F)
## PASE_Change           1    9.0  8.9787  0.7063 0.4013
## Age_sex               3   60.1 20.0428  1.5766 0.1950
## PASE_Change:Age_sex   3   30.1 10.0208  0.7883 0.5012
## Residuals           314 3991.7 12.7124

MAT

MAT_change_model_PA.1<- lm(MAT_Change~PASE_Change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(MAT_change_model_PA.1)
## 
## Call:
## lm(formula = MAT_Change ~ PASE_Change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.5245 -2.3930 -0.4553  1.3800 14.4812 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     2.931067   0.409846   7.152 6.92e-12 ***
## PASE_Change                    -0.008009   0.005520  -1.451    0.148    
## Age_sexFemales 65+             -0.243985   0.811992  -0.300    0.764    
## Age_sexMales 45-64             -3.231818   0.577223  -5.599 4.98e-08 ***
## Age_sexMales 65+               -3.386889   0.858017  -3.947 9.91e-05 ***
## PASE_Change:Age_sexFemales 65+ -0.027429   0.015973  -1.717    0.087 .  
## PASE_Change:Age_sexMales 45-64  0.007257   0.007795   0.931    0.353    
## PASE_Change:Age_sexMales 65+    0.015858   0.012792   1.240    0.216    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.147 on 292 degrees of freedom
##   (4881 observations deleted due to missingness)
## Multiple R-squared:  0.1333, Adjusted R-squared:  0.1126 
## F-statistic: 6.418 on 7 and 292 DF,  p-value: 4.891e-07
anova(MAT_change_model_PA.1)
## Analysis of Variance Table
## 
## Response: MAT_Change
##                      Df Sum Sq Mean Sq F value    Pr(>F)    
## PASE_Change           1   27.3  27.262  1.5851    0.2090    
## Age_sex               3  637.8 212.597 12.3605 1.237e-07 ***
## PASE_Change:Age_sex   3  107.6  35.871  2.0855    0.1022    
## Residuals           292 5022.3  17.200                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Animal Fluency

Animals_change_model_PA.1<- lm(Animal_Change~PASE_Change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(Animals_change_model_PA.1)
## 
## Call:
## lm(formula = Animal_Change ~ PASE_Change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.4504 -1.7331 -0.0457  1.7763  9.3279 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                    -0.521225   0.267526  -1.948   0.0522 .
## PASE_Change                     0.002793   0.003652   0.765   0.4450  
## Age_sexFemales 65+              0.344951   0.505639   0.682   0.4956  
## Age_sexMales 45-64              0.426153   0.371449   1.147   0.2521  
## Age_sexMales 65+                0.231333   0.532960   0.434   0.6645  
## PASE_Change:Age_sexFemales 65+  0.006526   0.010346   0.631   0.5286  
## PASE_Change:Age_sexMales 45-64 -0.005175   0.004990  -1.037   0.3004  
## PASE_Change:Age_sexMales 65+   -0.001358   0.008477  -0.160   0.8729  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.769 on 326 degrees of freedom
##   (4847 observations deleted due to missingness)
## Multiple R-squared:  0.009762,   Adjusted R-squared:  -0.0115 
## F-statistic: 0.4591 on 7 and 326 DF,  p-value: 0.8637
anova(Animals_change_model_PA.1)
## Analysis of Variance Table
## 
## Response: Animal_Change
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## PASE_Change           1    0.44  0.4351  0.0568 0.8118
## Age_sex               3    9.24  3.0787  0.4017 0.7519
## PASE_Change:Age_sex   3   14.96  4.9872  0.6507 0.5830
## Residuals           326 2498.77  7.6649

10.2.2) Changes in sedentary behaviour associated with changes in cognition

RVLT Immediate

RVLT_immediate_change_model_SB.1<- lm(RVLT_Immediate_Change~SB_binary_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(RVLT_immediate_change_model_SB.1)
## 
## Call:
## lm(formula = RVLT_Immediate_Change ~ SB_binary_change * Age_sex, 
##     data = subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16.4875  -2.2518   0.1472   2.0577  17.2289 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         -0.47608    0.12184  -3.907 9.46e-05 ***
## SB_binary_change                     0.29096    0.25111   1.159  0.24665    
## Age_sexFemales 65+                   0.59313    0.20397   2.908  0.00365 ** 
## Age_sexMales 45-64                  -0.05223    0.15920  -0.328  0.74285    
## Age_sexMales 65+                     0.69911    0.20488   3.412  0.00065 ***
## SB_binary_change:Age_sexFemales 65+  0.11937    0.42657   0.280  0.77961    
## SB_binary_change:Age_sexMales 45-64 -0.37323    0.33329  -1.120  0.26284    
## SB_binary_change:Age_sexMales 65+   -0.74773    0.42512  -1.759  0.07866 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.946 on 4759 degrees of freedom
##   (414 observations deleted due to missingness)
## Multiple R-squared:  0.007068,   Adjusted R-squared:  0.005607 
## F-statistic: 4.839 on 7 and 4759 DF,  p-value: 1.9e-05
anova(RVLT_immediate_change_model_SB.1)
## Analysis of Variance Table
## 
## Response: RVLT_Immediate_Change
##                            Df Sum Sq Mean Sq F value    Pr(>F)    
## SB_binary_change            1      2   2.067  0.1327    0.7156    
## Age_sex                     3    454 151.482  9.7275 2.137e-06 ***
## SB_binary_change:Age_sex    3     71  23.664  1.5196    0.2073    
## Residuals                4759  74110  15.573                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

RVLT Delayed

RVLT_delayed_change_model_SB.1<- lm(RVLT_Delayed_Change~SB_binary_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(RVLT_delayed_change_model_SB.1)
## 
## Call:
## lm(formula = RVLT_Delayed_Change ~ SB_binary_change * Age_sex, 
##     data = subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16.2208  -2.3429   0.3469   2.1510  18.1107 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                          -0.8383     0.1171  -7.159 9.42e-13 ***
## SB_binary_change                      0.1568     0.2412   0.650   0.5157    
## Age_sexFemales 65+                    0.7164     0.1980   3.618   0.0003 ***
## Age_sexMales 45-64                    0.0470     0.1532   0.307   0.7591    
## Age_sexMales 65+                      0.9639     0.1985   4.856 1.23e-06 ***
## SB_binary_change:Age_sexFemales 65+   0.1887     0.4138   0.456   0.6485    
## SB_binary_change:Age_sexMales 45-64  -0.2973     0.3215  -0.925   0.3553    
## SB_binary_change:Age_sexMales 65+    -0.6752     0.4128  -1.636   0.1020    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.778 on 4663 degrees of freedom
##   (510 observations deleted due to missingness)
## Multiple R-squared:  0.01029,    Adjusted R-squared:  0.008809 
## F-statistic: 6.929 on 7 and 4663 DF,  p-value: 3.139e-08
anova(RVLT_delayed_change_model_SB.1)
## Analysis of Variance Table
## 
## Response: RVLT_Delayed_Change
##                            Df Sum Sq Mean Sq F value    Pr(>F)    
## SB_binary_change            1      1   0.808  0.0566    0.8119    
## Age_sex                     3    631 210.375 14.7356 1.507e-09 ***
## SB_binary_change:Age_sex    3     61  20.184  1.4138    0.2367    
## Residuals                4663  66572  14.277                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

MAT

MAT_change_model_SB.1<- lm(MAT_Change~SB_binary_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(MAT_change_model_SB.1)
## 
## Call:
## lm(formula = MAT_Change ~ SB_binary_change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.0290  -2.6200  -0.5787   1.4452  19.4113 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                          3.35292    0.13667  24.533  < 2e-16 ***
## SB_binary_change                    -0.16545    0.28074  -0.589    0.556    
## Age_sexFemales 65+                  -0.97541    0.23697  -4.116 3.93e-05 ***
## Age_sexMales 45-64                  -3.38379    0.17926 -18.877  < 2e-16 ***
## Age_sexMales 65+                    -2.89547    0.24224 -11.953  < 2e-16 ***
## SB_binary_change:Age_sexFemales 65+  0.31414    0.50114   0.627    0.531    
## SB_binary_change:Age_sexMales 45-64  0.09336    0.37399   0.250    0.803    
## SB_binary_change:Age_sexMales 65+   -0.41052    0.50366  -0.815    0.415    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.219 on 4122 degrees of freedom
##   (1051 observations deleted due to missingness)
## Multiple R-squared:  0.1141, Adjusted R-squared:  0.1126 
## F-statistic: 75.87 on 7 and 4122 DF,  p-value: < 2.2e-16
anova(MAT_change_model_SB.1)
## Analysis of Variance Table
## 
## Response: MAT_Change
##                            Df Sum Sq Mean Sq  F value Pr(>F)    
## SB_binary_change            1      7    6.50   0.3653 0.5456    
## Age_sex                     3   9418 3139.23 176.3561 <2e-16 ***
## SB_binary_change:Age_sex    3     29    9.82   0.5515 0.6471    
## Residuals                4122  73374   17.80                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Animals Fluency

Animals_change_model_SB.1<- lm(Animal_Change~SB_binary_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(Animals_change_model_SB.1)
## 
## Call:
## lm(formula = Animal_Change ~ SB_binary_change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.2424  -1.7815  -0.0367   1.7589  13.8514 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         -0.24709    0.08621  -2.866  0.00417 ** 
## SB_binary_change                    -0.09330    0.17799  -0.524  0.60015    
## Age_sexFemales 65+                   0.32611    0.14388   2.266  0.02347 *  
## Age_sexMales 45-64                   0.21566    0.11270   1.914  0.05573 .  
## Age_sexMales 65+                     0.56171    0.14420   3.895 9.93e-05 ***
## SB_binary_change:Age_sexFemales 65+  0.38551    0.30060   1.282  0.19974    
## SB_binary_change:Age_sexMales 45-64 -0.06441    0.23668  -0.272  0.78553    
## SB_binary_change:Age_sexMales 65+   -0.02126    0.30031  -0.071  0.94355    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.803 on 4803 degrees of freedom
##   (370 observations deleted due to missingness)
## Multiple R-squared:  0.005412,   Adjusted R-squared:  0.003962 
## F-statistic: 3.733 on 7 and 4803 DF,  p-value: 0.0004881
anova(Animals_change_model_SB.1)
## Analysis of Variance Table
## 
## Response: Animal_Change
##                            Df Sum Sq Mean Sq F value    Pr(>F)    
## SB_binary_change            1      3   3.215  0.4092    0.5224    
## Age_sex                     3    182  60.575  7.7108 3.888e-05 ***
## SB_binary_change:Age_sex    3     20   6.788  0.8641    0.4589    
## Residuals                4803  37732   7.856                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

10.2.3) Changes in sleep associated with changes in cognition

RVLT Immediate

RVLT_immediate_change_model_sleep.1<- lm(RVLT_Immediate_Change~Sleep_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(RVLT_immediate_change_model_sleep.1)
## 
## Call:
## lm(formula = RVLT_Immediate_Change ~ Sleep_change * Age_sex, 
##     data = subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16.1376  -2.2236   0.1041   2.1095  17.3691 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     -0.43296    0.11604  -3.731 0.000193 ***
## Sleep_change                     0.12587    0.28804   0.437 0.662143    
## Age_sexFemales 65+               0.61052    0.19561   3.121 0.001813 ** 
## Age_sexMales 45-64              -0.05658    0.15140  -0.374 0.708657    
## Age_sexMales 65+                 0.51584    0.19250   2.680 0.007395 ** 
## Sleep_change:Age_sexFemales 65+  0.24314    0.47938   0.507 0.612044    
## Sleep_change:Age_sexMales 45-64 -0.55202    0.38735  -1.425 0.154188    
## Sleep_change:Age_sexMales 65+    0.17218    0.52982   0.325 0.745203    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.937 on 4772 degrees of freedom
##   (401 observations deleted due to missingness)
## Multiple R-squared:  0.007391,   Adjusted R-squared:  0.005935 
## F-statistic: 5.076 on 7 and 4772 DF,  p-value: 9.318e-06
anova(RVLT_immediate_change_model_sleep.1)
## Analysis of Variance Table
## 
## Response: RVLT_Immediate_Change
##                        Df Sum Sq Mean Sq F value    Pr(>F)    
## Sleep_change            1      0   0.239  0.0154    0.9012    
## Age_sex                 3    485 161.507 10.4204 7.856e-07 ***
## Sleep_change:Age_sex    3     66  21.998  1.4193    0.2351    
## Residuals            4772  73962  15.499                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

RVLT Delayed

RVLT_delayed_change_model_sleep.1<- lm(RVLT_Delayed_Change~Sleep_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(RVLT_delayed_change_model_sleep.1)
## 
## Call:
## lm(formula = RVLT_Delayed_Change ~ Sleep_change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.049  -2.343   0.334   2.111  18.078 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     -0.83792    0.11166  -7.504 7.36e-14 ***
## Sleep_change                     0.22467    0.27740   0.810    0.418    
## Age_sexFemales 65+               0.74840    0.19042   3.930 8.61e-05 ***
## Age_sexMales 45-64               0.05946    0.14598   0.407    0.684    
## Age_sexMales 65+                 0.79120    0.18713   4.228 2.40e-05 ***
## Sleep_change:Age_sexFemales 65+  0.07790    0.46342   0.168    0.867    
## Sleep_change:Age_sexMales 45-64 -0.59867    0.37442  -1.599    0.110    
## Sleep_change:Age_sexMales 65+    0.24993    0.50954   0.491    0.624    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.775 on 4675 degrees of freedom
##   (498 observations deleted due to missingness)
## Multiple R-squared:  0.01066,    Adjusted R-squared:  0.009181 
## F-statistic: 7.197 on 7 and 4675 DF,  p-value: 1.356e-08
anova(RVLT_delayed_change_model_sleep.1)
## Analysis of Variance Table
## 
## Response: RVLT_Delayed_Change
##                        Df Sum Sq Mean Sq F value    Pr(>F)    
## Sleep_change            1      1   1.005  0.0705    0.7906    
## Age_sex                 3    650 216.734 15.2081 7.578e-10 ***
## Sleep_change:Age_sex    3     67  22.267  1.5625    0.1963    
## Residuals            4675  66624  14.251                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

MAT

MAT_change_model_sleep.1<- lm(MAT_Change~Sleep_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(MAT_change_model_sleep.1)
## 
## Call:
## lm(formula = MAT_Change ~ Sleep_change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.0040  -2.6143  -0.5559   1.4415  19.4158 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      3.34834    0.13055  25.649  < 2e-16 ***
## Sleep_change                    -0.24040    0.32236  -0.746 0.455860    
## Age_sexFemales 65+              -0.87980    0.22826  -3.854 0.000118 ***
## Age_sexMales 45-64              -3.40194    0.17083 -19.914  < 2e-16 ***
## Age_sexMales 65+                -3.02126    0.22721 -13.297  < 2e-16 ***
## Sleep_change:Age_sexFemales 65+ -0.04056    0.56309  -0.072 0.942574    
## Sleep_change:Age_sexMales 45-64  0.29540    0.43691   0.676 0.499000    
## Sleep_change:Age_sexMales 65+    0.34584    0.64899   0.533 0.594142    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.22 on 4133 degrees of freedom
##   (1040 observations deleted due to missingness)
## Multiple R-squared:  0.1135, Adjusted R-squared:  0.112 
## F-statistic: 75.58 on 7 and 4133 DF,  p-value: < 2.2e-16
anova(MAT_change_model_sleep.1)
## Analysis of Variance Table
## 
## Response: MAT_Change
##                        Df Sum Sq Mean Sq  F value Pr(>F)    
## Sleep_change            1      4    4.00   0.2244 0.6357    
## Age_sex                 3   9405 3135.13 176.0370 <2e-16 ***
## Sleep_change:Age_sex    3     13    4.46   0.2503 0.8612    
## Residuals            4133  73607   17.81                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Animal Fluency

Animals_change_model_sleep.1<- lm(Animal_Change~Sleep_change*Age_sex, data=subset(Tracking.Adjusted_Final, Pandemic == "FU2 data collected after COVID-19"))
summary(Animals_change_model_sleep.1)
## 
## Call:
## lm(formula = Animal_Change ~ Sleep_change * Age_sex, data = subset(Tracking.Adjusted_Final, 
##     Pandemic == "FU2 data collected after COVID-19"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.2058  -1.8207  -0.0352   1.7543  13.9079 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     -0.25959    0.08241  -3.150  0.00164 ** 
## Sleep_change                    -0.05861    0.20524  -0.286  0.77521    
## Age_sexFemales 65+               0.42851    0.13842   3.096  0.00198 ** 
## Age_sexMales 45-64               0.17162    0.10766   1.594  0.11098    
## Age_sexMales 65+                 0.57272    0.13608   4.209 2.61e-05 ***
## Sleep_change:Age_sexFemales 65+ -0.06783    0.33926  -0.200  0.84155    
## Sleep_change:Age_sexMales 45-64  0.27989    0.27587   1.015  0.31036    
## Sleep_change:Age_sexMales 65+   -0.14000    0.37602  -0.372  0.70967    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.807 on 4816 degrees of freedom
##   (357 observations deleted due to missingness)
## Multiple R-squared:  0.005224,   Adjusted R-squared:  0.003778 
## F-statistic: 3.613 on 7 and 4816 DF,  p-value: 0.0006883
anova(Animals_change_model_sleep.1)
## Analysis of Variance Table
## 
## Response: Animal_Change
##                        Df Sum Sq Mean Sq F value    Pr(>F)    
## Sleep_change            1      0   0.011  0.0014    0.9707    
## Age_sex                 3    183  60.869  7.7255 3.806e-05 ***
## Sleep_change:Age_sex    3     17   5.552  0.7047    0.5491    
## Residuals            4816  37945   7.879                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11) Sensitivity Analyses

11.1) Truncated Sample Development

For this sensitivity analysis, we only include participants w/ data collected in 2019 onward (N=16,649)

startdate <- as.POSIXct("2019-01-01 00:00:00", tz = "EST")
truncated <- subset(Tracking.Adjusted_Final, timestamp>startdate)

Linear mixed model set-up

truncated.short<-truncated[c(1,4:12,14,15,18:21,146,147,98,115,132,102,119,136,106,123,140,110,127,144,13,44,75,22,53,84,23,54,85)]

truncated.short2<-rename(truncated.short, c("Age"="Age_0","Sex"="Sex_0","Ethnicity"="Ethnicity_0","Relationshipstatus"="Relationship_status_0",
                             "Education"="Education4_0", "IncomeLevel"="Income_Level_0", "Livingstatus"="Living_status_0", 
                             "Alcohol"="Alcohol_0", "SmokingStatus"="Smoking_Status_0","Anxiety"="Anxiety_0","MoodDisord"="Mood_Disord_0",
                             "Chronicconditions"="Chronic_conditions_0", "BMI"="BMI_0","PASE_Sit_0"="PASE_Q1B_0","PASE_Sit_1"="PASE_Q1B_1","PASE_Sit_2"="PASE_Q1B_2"))

truncated.short2$PASE_TOTALbaseline <- truncated.short2$PASE_TOTAL_0

truncated.short3<-truncated.short2[c(1:18,40,19:39)]

truncated.short4<-truncated.short3[c(1:18,20,23,26,29,32,35,38,21:22,24:25,27:28,30:31,33:34,36:37,39:40)]

colnames(truncated.short3) <- (gsub("_2",".3",colnames(truncated.short3)))
colnames(truncated.short3) <- (gsub("_1",".2",colnames(truncated.short3)))
colnames(truncated.short3) <- (gsub("_0",".1",colnames(truncated.short3)))

colnames(truncated.short4) <- (gsub("_2",".2",colnames(truncated.short4)))
colnames(truncated.short4) <- (gsub("_1",".1",colnames(truncated.short4)))
colnames(truncated.short4) <- (gsub("_0","baseline",colnames(truncated.short4)))


truncated.data_long <- reshape(as.data.frame(truncated.short3),idvar="ID",varying=20:40,direction="long",sep=".") #reshape data into long format (3 timepoints)
truncated.data_long_2 <- reshape(as.data.frame(truncated.short4),idvar="ID",varying=26:39,direction="long",sep=".") #reshape data into long format (3 timepoints)

Indexed time as a categorical factor

#Treat time as a fixed effect
truncated.data_long$timefactor<-as.factor(truncated.data_long$time)
truncated.data_long_2$timefactor<-as.factor(truncated.data_long_2$time)

Age and Sex grouping

truncated.data_long$Age_sex<-NA
truncated.data_long$Age_sex[truncated.data_long$Age<=64 & truncated.data_long$Sex == "M"]<-"Males 45-64"
truncated.data_long$Age_sex[truncated.data_long$Age<=64 & truncated.data_long$Sex == "F"]<-"Females 45-64"
truncated.data_long$Age_sex[truncated.data_long$Age>64 & truncated.data_long$Sex == "M"]<-"Males 65+"
truncated.data_long$Age_sex[truncated.data_long$Age>64 & truncated.data_long$Sex == "F"]<-"Females 65+"

truncated.data_long_2$Age_sex<-NA
truncated.data_long_2$Age_sex[truncated.data_long_2$Age<=64 & truncated.data_long_2$Sex == "M"]<-"Males 45-64"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age<=64 & truncated.data_long_2$Sex == "F"]<-"Females 45-64"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age>64 & truncated.data_long_2$Sex == "M"]<-"Males 65+"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age>64 & truncated.data_long_2$Sex == "F"]<-"Females 65+"

11.2) Truncated Sample Characteristics

Truncated full sample (N= 16,649)

Baseline<-dput(names(truncated[c(5,4,14,12,6,7,8,9,10,11,15,18,19,20,13,28,30,26,23)]))
## c("Age_0", "Sex_0", "BMI_0", "Ethnicity_0", "Relationship_status_0", 
## "Education4_0", "Income_Level_0", "Living_status_0", "Alcohol_0", 
## "Smoking_Status_0", "CESD_10_0", "Anxiety_0", "Mood_Disord_0", 
## "Pet_Owner_0", "PASE_TOTAL_0", "MAT_Score_0", "RVLT_Immediate_Score_0", 
## "Animal_Fluency_Lenient_0", "RSTLS_Sleep_0")
Table1_truncated<-CreateTableOne(vars=Baseline, data=truncated)
print(Table1_truncated,contDigits=2,missing=TRUE,quote=TRUE)
##                                               ""
##  ""                                            "Overall"        "Missing"
##   "n"                                          "  9423"         "    "   
##   "Age_0 (mean (SD))"                          " 61.17 (10.22)" " 0.0"   
##   "Sex_0 = M (%)"                              "  4624 (49.1) " " 0.0"   
##   "BMI_0 (mean (SD))"                          " 27.48 (5.05)"  " 0.5"   
##   "Ethnicity_0 = White (%)"                    "  9165 (97.3) " " 0.0"   
##   "Relationship_status_0 (%)"                  "  "             " 0.0"   
##   "   Divorced"                                "   813 ( 8.6) " "    "   
##   "   Married"                                 "  6900 (73.3) " "    "   
##   "   Separated"                               "   246 ( 2.6) " "    "   
##   "   Single"                                  "   694 ( 7.4) " "    "   
##   "   Widowed"                                 "   766 ( 8.1) " "    "   
##   "Education4_0 (%)"                           "  "             " 0.0"   
##   "   College Degree or Higher"                "  6879 (73.0) " "    "   
##   "   High School Diploma"                     "  1213 (12.9) " "    "   
##   "   Less than High School Diploma"           "   626 ( 6.6) " "    "   
##   "   Some College"                            "   705 ( 7.5) " "    "   
##   "Income_Level_0 (%)"                         "  "             " 3.6"   
##   "   <$20k"                                   "  1449 (16.0) " "    "   
##   "   >$150k"                                  "   380 ( 4.2) " "    "   
##   "   $100-150k"                               "   715 ( 7.9) " "    "   
##   "   $20-50k"                                 "  3534 (38.9) " "    "   
##   "   $50-100k"                                "  3004 (33.1) " "    "   
##   "Living_status_0 (%)"                        "  "             " 0.0"   
##   "   Apartment/Condo/Townhome"                "  1063 (11.3) " "    "   
##   "   Assisted Living"                         "    48 ( 0.5) " "    "   
##   "   House"                                   "  8233 (87.4) " "    "   
##   "   Other"                                   "    79 ( 0.8) " "    "   
##   "Alcohol_0 (%)"                              "  "             " 3.0"   
##   "   Non-drinker"                             "   929 (10.2) " "    "   
##   "   Occasional drinker"                      "  1439 (15.7) " "    "   
##   "   Regular drinker (at least once a month)" "  6773 (74.1) " "    "   
##   "Smoking_Status_0 (%)"                       "  "             " 0.5"   
##   "   Daily Smoker"                            "   628 ( 6.7) " "    "   
##   "   Former Smoker"                           "  5667 (60.4) " "    "   
##   "   Never Smoked"                            "  2927 (31.2) " "    "   
##   "   Occasional Smoker"                       "   156 ( 1.7) " "    "   
##   "CESD_10_0 (mean (SD))"                      "  4.97 (4.37)"  " 0.3"   
##   "Anxiety_0 = Yes (%)"                        "   598 ( 6.4) " " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    "  1266 (13.4) " " 0.1"   
##   "Pet_Owner_0 = Yes (%)"                      "  4526 (48.2) " " 0.4"   
##   "PASE_TOTAL_0 (mean (SD))"                   "172.78 (79.33)" "19.2"   
##   "MAT_Score_0 (mean (SD))"                    " 27.23 (9.14)"  " 0.0"   
##   "RVLT_Immediate_Score_0 (mean (SD))"         "  6.15 (2.25)"  " 0.0"   
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 22.08 (6.38)"  " 0.0"   
##   "RSTLS_Sleep_0 (mean (SD))"                  "  0.33 (0.47)"  " 0.2"

Final baseline sample stratified by whether FU2 data was collected before (N= 7132) or after (N= 6898) the start of the COVID-19 pandemic

Table1_truncated_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=truncated)
print(Table1_truncated_stratify,contDigits=2,missing=TRUE,quote=TRUE)
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  5181"                           
##   "Age_0 (mean (SD))"                          " 60.29 (10.54)"                   
##   "Sex_0 = M (%)"                              "  2856 (55.1) "                   
##   "BMI_0 (mean (SD))"                          " 27.52 (4.94)"                    
##   "Ethnicity_0 = White (%)"                    "  5025 (97.0) "                   
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   408 ( 7.9) "                   
##   "   Married"                                 "  3841 (74.2) "                   
##   "   Separated"                               "   150 ( 2.9) "                   
##   "   Single"                                  "   383 ( 7.4) "                   
##   "   Widowed"                                 "   397 ( 7.7) "                   
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  3605 (69.6) "                   
##   "   High School Diploma"                     "   776 (15.0) "                   
##   "   Less than High School Diploma"           "   411 ( 7.9) "                   
##   "   Some College"                            "   389 ( 7.5) "                   
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   783 (15.6) "                   
##   "   >$150k"                                  "   239 ( 4.8) "                   
##   "   $100-150k"                               "   426 ( 8.5) "                   
##   "   $20-50k"                                 "  1874 (37.3) "                   
##   "   $50-100k"                                "  1700 (33.9) "                   
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   579 (11.2) "                   
##   "   Assisted Living"                         "    24 ( 0.5) "                   
##   "   House"                                   "  4541 (87.6) "                   
##   "   Other"                                   "    37 ( 0.7) "                   
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   512 (10.2) "                   
##   "   Occasional drinker"                      "   784 (15.6) "                   
##   "   Regular drinker (at least once a month)" "  3731 (74.2) "                   
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   361 ( 7.0) "                   
##   "   Former Smoker"                           "  3128 (60.6) "                   
##   "   Never Smoked"                            "  1579 (30.6) "                   
##   "   Occasional Smoker"                       "    90 ( 1.7) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.08 (4.47)"                    
##   "Anxiety_0 = Yes (%)"                        "   335 ( 6.5) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   698 (13.5) "                   
##   "Pet_Owner_0 = Yes (%)"                      "  2562 (49.6) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "179.56 (81.39)"                   
##   "MAT_Score_0 (mean (SD))"                    " 27.11 (9.20)"                    
##   "RVLT_Immediate_Score_0 (mean (SD))"         "  5.98 (2.20)"                    
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 21.92 (6.41)"                    
##   "RSTLS_Sleep_0 (mean (SD))"                  "  0.33 (0.47)"                    
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  4242"                            
##   "Age_0 (mean (SD))"                          " 62.24 (9.72)"                     
##   "Sex_0 = M (%)"                              "  1768 (41.7) "                    
##   "BMI_0 (mean (SD))"                          " 27.43 (5.19)"                     
##   "Ethnicity_0 = White (%)"                    "  4140 (97.6) "                    
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   405 ( 9.6) "                    
##   "   Married"                                 "  3059 (72.1) "                    
##   "   Separated"                               "    96 ( 2.3) "                    
##   "   Single"                                  "   311 ( 7.3) "                    
##   "   Widowed"                                 "   369 ( 8.7) "                    
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  3274 (77.2) "                    
##   "   High School Diploma"                     "   437 (10.3) "                    
##   "   Less than High School Diploma"           "   215 ( 5.1) "                    
##   "   Some College"                            "   316 ( 7.4) "                    
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   666 (16.4) "                    
##   "   >$150k"                                  "   141 ( 3.5) "                    
##   "   $100-150k"                               "   289 ( 7.1) "                    
##   "   $20-50k"                                 "  1660 (40.9) "                    
##   "   $50-100k"                                "  1304 (32.1) "                    
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   484 (11.4) "                    
##   "   Assisted Living"                         "    24 ( 0.6) "                    
##   "   House"                                   "  3692 (87.0) "                    
##   "   Other"                                   "    42 ( 1.0) "                    
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   417 (10.1) "                    
##   "   Occasional drinker"                      "   655 (15.9) "                    
##   "   Regular drinker (at least once a month)" "  3042 (73.9) "                    
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   267 ( 6.3) "                    
##   "   Former Smoker"                           "  2539 (60.2) "                    
##   "   Never Smoked"                            "  1348 (31.9) "                    
##   "   Occasional Smoker"                       "    66 ( 1.6) "                    
##   "CESD_10_0 (mean (SD))"                      "  4.83 (4.24)"                     
##   "Anxiety_0 = Yes (%)"                        "   263 ( 6.2) "                    
##   "Mood_Disord_0 = Yes (%)"                    "   568 (13.4) "                    
##   "Pet_Owner_0 = Yes (%)"                      "  1964 (46.5) "                    
##   "PASE_TOTAL_0 (mean (SD))"                   "164.63 (76.01)"                    
##   "MAT_Score_0 (mean (SD))"                    " 27.37 (9.06)"                     
##   "RVLT_Immediate_Score_0 (mean (SD))"         "  6.35 (2.28)"                     
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 22.28 (6.34)"                     
##   "RSTLS_Sleep_0 (mean (SD))"                  "  0.33 (0.47)"                     
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "<0.001" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.378" ""     " 0.5"   
##   "Ethnicity_0 = White (%)"                    " 0.083" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.004" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         "<0.001" ""     " 3.6"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.421" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.914" ""     " 3.0"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.331" ""     " 0.5"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.006" ""     " 0.3"   
##   "Anxiety_0 = Yes (%)"                        " 0.624" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.940" ""     " 0.1"   
##   "Pet_Owner_0 = Yes (%)"                      " 0.003" ""     " 0.4"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "19.2"   
##   "MAT_Score_0 (mean (SD))"                    " 0.168" ""     " 0.0"   
##   "RVLT_Immediate_Score_0 (mean (SD))"         "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 0.006" ""     " 0.0"   
##   "RSTLS_Sleep_0 (mean (SD))"                  " 0.804" ""     " 0.2"

11.3) Main Effects Model

All models use normalized cognitive scores. Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

11.3.1) RVLT Immediate Recall

11.3.1.1) Model

modelRVLT_imm_adj10trun<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_imm_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 70686.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6998 -0.5714 -0.0379  0.5273  3.8358 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 3.951    1.988   
##  Residual             7.521    2.743   
## Number of obs: 13535, groups:  ID, 6973
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.310e+00  4.650e-01
## timefactor2                                             3.109e-01  6.425e-02
## PandemicFU2 data collected before COVID-19              1.741e-01  8.368e-02
## Age                                                    -1.625e-02  4.367e-03
## SexM                                                   -5.030e-01  7.602e-02
## EducationHigh School Diploma                            2.889e-01  1.063e-01
## EducationLess than High School Diploma                  4.337e-01  1.512e-01
## EducationSome College                                   1.433e-01  1.319e-01
## EthnicityWhite                                          6.392e-01  2.106e-01
## IncomeLevel>$150k                                       3.879e-01  1.947e-01
## IncomeLevel$100-150k                                    2.799e-01  1.578e-01
## IncomeLevel$20-50k                                      1.605e-01  1.064e-01
## IncomeLevel$50-100k                                     4.062e-01  1.145e-01
## BMI                                                    -1.609e-02  7.173e-03
## CESD.10baseline                                        -2.310e-02  8.556e-03
## SmokingStatusFormer Smoker                              2.334e-01  1.450e-01
## SmokingStatusNever Smoked                               3.461e-01  1.508e-01
## SmokingStatusOccasional Smoker                          4.729e-02  2.850e-01
## RelationshipstatusMarried                               3.348e-01  1.245e-01
## RelationshipstatusSeparated                             3.097e-01  2.415e-01
## RelationshipstatusSingle                                2.316e-01  1.689e-01
## RelationshipstatusWidowed                               3.257e-02  1.729e-01
## LivingstatusAssisted Living                            -9.343e-01  5.249e-01
## LivingstatusHouse                                      -3.798e-02  1.136e-01
## LivingstatusOther                                      -5.045e-01  4.209e-01
## AnxietyYes                                              5.017e-02  1.486e-01
## MoodDisordYes                                          -1.114e-01  1.073e-01
## Chronicconditions                                      -2.421e-02  1.754e-02
## PASE_TOTALbaseline                                      2.198e-03  5.041e-04
## RVLT_Immediate_Normedbaseline                           3.554e-01  8.988e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -7.979e-02  9.517e-02
##                                                                df t value
## (Intercept)                                             6.954e+03  15.720
## timefactor2                                             6.830e+03   4.839
## PandemicFU2 data collected before COVID-19              1.203e+04   2.081
## Age                                                     6.915e+03  -3.721
## SexM                                                    6.911e+03  -6.617
## EducationHigh School Diploma                            6.917e+03   2.718
## EducationLess than High School Diploma                  7.040e+03   2.869
## EducationSome College                                   6.839e+03   1.086
## EthnicityWhite                                          6.840e+03   3.035
## IncomeLevel>$150k                                       6.916e+03   1.992
## IncomeLevel$100-150k                                    6.873e+03   1.774
## IncomeLevel$20-50k                                      6.924e+03   1.508
## IncomeLevel$50-100k                                     6.921e+03   3.547
## BMI                                                     6.887e+03  -2.244
## CESD.10baseline                                         6.911e+03  -2.699
## SmokingStatusFormer Smoker                              6.937e+03   1.610
## SmokingStatusNever Smoked                               6.937e+03   2.295
## SmokingStatusOccasional Smoker                          6.807e+03   0.166
## RelationshipstatusMarried                               6.926e+03   2.690
## RelationshipstatusSeparated                             6.989e+03   1.282
## RelationshipstatusSingle                                6.926e+03   1.371
## RelationshipstatusWidowed                               6.931e+03   0.188
## LivingstatusAssisted Living                             6.929e+03  -1.780
## LivingstatusHouse                                       6.923e+03  -0.334
## LivingstatusOther                                       6.794e+03  -1.199
## AnxietyYes                                              6.894e+03   0.338
## MoodDisordYes                                           6.909e+03  -1.038
## Chronicconditions                                       6.902e+03  -1.381
## PASE_TOTALbaseline                                      6.898e+03   4.360
## RVLT_Immediate_Normedbaseline                           6.927e+03  39.546
## timefactor2:PandemicFU2 data collected before COVID-19  6.778e+03  -0.838
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.33e-06 ***
## PandemicFU2 data collected before COVID-19             0.037474 *  
## Age                                                    0.000200 ***
## SexM                                                   3.93e-11 ***
## EducationHigh School Diploma                           0.006591 ** 
## EducationLess than High School Diploma                 0.004131 ** 
## EducationSome College                                  0.277388    
## EthnicityWhite                                         0.002417 ** 
## IncomeLevel>$150k                                      0.046385 *  
## IncomeLevel$100-150k                                   0.076170 .  
## IncomeLevel$20-50k                                     0.131549    
## IncomeLevel$50-100k                                    0.000392 ***
## BMI                                                    0.024890 *  
## CESD.10baseline                                        0.006964 ** 
## SmokingStatusFormer Smoker                             0.107355    
## SmokingStatusNever Smoked                              0.021759 *  
## SmokingStatusOccasional Smoker                         0.868203    
## RelationshipstatusMarried                              0.007166 ** 
## RelationshipstatusSeparated                            0.199713    
## RelationshipstatusSingle                               0.170338    
## RelationshipstatusWidowed                              0.850629    
## LivingstatusAssisted Living                            0.075114 .  
## LivingstatusHouse                                      0.738135    
## LivingstatusOther                                      0.230711    
## AnxietyYes                                             0.735738    
## MoodDisordYes                                          0.299306    
## Chronicconditions                                      0.167449    
## PASE_TOTALbaseline                                     1.32e-05 ***
## RVLT_Immediate_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.401822    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_imm_adj10trun)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)
## timefactor                      244.0   244.0     1 6778.2   32.4367 1.283e-08
## Pandemic                         28.3    28.3     1 6897.2    3.7611  0.052498
## Age                             104.1   104.1     1 6914.6   13.8463  0.000200
## Sex                             329.4   329.4     1 6910.9   43.7912 3.927e-11
## Education                       103.7    34.6     3 6931.7    4.5952  0.003231
## Ethnicity                        69.3    69.3     1 6840.4    9.2094  0.002417
## IncomeLevel                     114.7    28.7     4 6895.7    3.8123  0.004239
## BMI                              37.9    37.9     1 6887.4    5.0337  0.024890
## CESD.10baseline                  54.8    54.8     1 6910.7    7.2866  0.006964
## SmokingStatus                    49.2    16.4     3 6868.7    2.1821  0.087960
## Relationshipstatus               78.6    19.6     4 6950.2    2.6123  0.033591
## Livingstatus                     33.5    11.2     3 6880.0    1.4850  0.216435
## Anxiety                           0.9     0.9     1 6893.7    0.1139  0.735738
## MoodDisord                        8.1     8.1     1 6909.1    1.0774  0.299306
## Chronicconditions                14.3    14.3     1 6902.4    1.9060  0.167449
## PASE_TOTALbaseline              143.0   143.0     1 6898.1   19.0081 1.321e-05
## RVLT_Immediate_Normedbaseline 11762.4 11762.4     1 6926.8 1563.8757 < 2.2e-16
## timefactor:Pandemic               5.3     5.3     1 6778.3    0.7030  0.401822
##                                  
## timefactor                    ***
## Pandemic                      .  
## Age                           ***
## Sex                           ***
## Education                     ** 
## Ethnicity                     ** 
## IncomeLevel                   ** 
## BMI                           *  
## CESD.10baseline               ** 
## SmokingStatus                 .  
## Relationshipstatus            *  
## Livingstatus                     
## Anxiety                          
## MoodDisord                       
## Chronicconditions                
## PASE_TOTALbaseline            ***
## RVLT_Immediate_Normedbaseline ***
## timefactor:Pandemic              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.3.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.26540 0.2305358 7120.00  9.813486
##  FU2 data collected before COVID-19 10.43953 0.2337416 7173.12  9.981331
##  upper.CL
##  10.71732
##  10.89774
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.57632 0.2307404 7136.48 10.124004
##  FU2 data collected before COVID-19 10.67066 0.2337331 7172.52 10.212472
##  upper.CL
##  11.02864
##  11.12884
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##  -0.17412870 0.08368418 12029.21  -2.081  0.0375
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##  -0.09433404 0.08430367 12106.33  -1.119  0.2632
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL    upper.CL
##  -0.17412870 0.08368418 12029.21 -0.3381632 -0.01009421
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL    upper.CL
##  -0.09433404 0.08430367 12106.33 -0.2595827  0.07091464
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.3.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj10trun <- summary(lsmeans(modelRVLT_imm_adj10trun, ~timefactor|Pandemic))
RVLTimmediate_lsmeans_adj10trun$Time<-NA
RVLTimmediate_lsmeans_adj10trun$Time[RVLTimmediate_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj10trun$Time[RVLTimmediate_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

11.3.2) RVLT Delayed Recall

11.3.2.1) Model

modelRVLT_del_adj10trun<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_del_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 69370.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9097 -0.5557 -0.0353  0.5142  4.0193 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.094    2.023   
##  Residual             6.916    2.630   
## Number of obs: 13415, groups:  ID, 6961
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             6.692e+00  4.627e-01
## timefactor2                                             5.846e-01  6.205e-02
## PandemicFU2 data collected before COVID-19              2.559e-01  8.231e-02
## Age                                                    -8.322e-03  4.343e-03
## SexM                                                   -4.549e-01  7.530e-02
## EducationHigh School Diploma                            2.210e-01  1.056e-01
## EducationLess than High School Diploma                  2.909e-01  1.506e-01
## EducationSome College                                   3.171e-01  1.307e-01
## EthnicityWhite                                          6.681e-01  2.104e-01
## IncomeLevel>$150k                                       2.581e-01  1.929e-01
## IncomeLevel$100-150k                                    1.625e-01  1.566e-01
## IncomeLevel$20-50k                                      1.723e-01  1.057e-01
## IncomeLevel$50-100k                                     4.387e-01  1.136e-01
## BMI                                                    -1.830e-02  7.112e-03
## CESD.10baseline                                        -2.062e-02  8.500e-03
## SmokingStatusFormer Smoker                              1.293e-01  1.436e-01
## SmokingStatusNever Smoked                               3.377e-01  1.494e-01
## SmokingStatusOccasional Smoker                          2.223e-01  2.831e-01
## RelationshipstatusMarried                               3.936e-02  1.237e-01
## RelationshipstatusSeparated                            -1.141e-01  2.398e-01
## RelationshipstatusSingle                               -5.020e-02  1.676e-01
## RelationshipstatusWidowed                              -1.397e-01  1.718e-01
## LivingstatusAssisted Living                            -1.257e+00  5.220e-01
## LivingstatusHouse                                       4.488e-02  1.128e-01
## LivingstatusOther                                      -1.792e-01  4.192e-01
## AnxietyYes                                              5.259e-02  1.476e-01
## MoodDisordYes                                          -1.189e-01  1.064e-01
## Chronicconditions                                      -2.792e-02  1.740e-02
## PASE_TOTALbaseline                                      2.433e-03  5.004e-04
## RVLT_Delayed_Normedbaseline                             3.953e-01  9.114e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.551e-01  9.182e-02
##                                                                df t value
## (Intercept)                                             6.969e+03  14.462
## timefactor2                                             6.770e+03   9.421
## PandemicFU2 data collected before COVID-19              1.178e+04   3.109
## Age                                                     6.907e+03  -1.916
## SexM                                                    6.890e+03  -6.042
## EducationHigh School Diploma                            6.904e+03   2.093
## EducationLess than High School Diploma                  7.068e+03   1.932
## EducationSome College                                   6.802e+03   2.427
## EthnicityWhite                                          6.926e+03   3.175
## IncomeLevel>$150k                                       6.891e+03   1.338
## IncomeLevel$100-150k                                    6.858e+03   1.038
## IncomeLevel$20-50k                                      6.917e+03   1.630
## IncomeLevel$50-100k                                     6.901e+03   3.860
## BMI                                                     6.849e+03  -2.573
## CESD.10baseline                                         6.915e+03  -2.426
## SmokingStatusFormer Smoker                              6.902e+03   0.901
## SmokingStatusNever Smoked                               6.902e+03   2.260
## SmokingStatusOccasional Smoker                          6.830e+03   0.785
## RelationshipstatusMarried                               6.890e+03   0.318
## RelationshipstatusSeparated                             6.999e+03  -0.476
## RelationshipstatusSingle                                6.882e+03  -0.300
## RelationshipstatusWidowed                               6.912e+03  -0.813
## LivingstatusAssisted Living                             6.970e+03  -2.407
## LivingstatusHouse                                       6.921e+03   0.398
## LivingstatusOther                                       6.858e+03  -0.428
## AnxietyYes                                              6.870e+03   0.356
## MoodDisordYes                                           6.877e+03  -1.118
## Chronicconditions                                       6.874e+03  -1.604
## PASE_TOTALbaseline                                      6.889e+03   4.863
## RVLT_Delayed_Normedbaseline                             6.894e+03  43.370
## timefactor2:PandemicFU2 data collected before COVID-19  6.710e+03  -2.778
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             0.001883 ** 
## Age                                                    0.055388 .  
## SexM                                                   1.60e-09 ***
## EducationHigh School Diploma                           0.036387 *  
## EducationLess than High School Diploma                 0.053383 .  
## EducationSome College                                  0.015265 *  
## EthnicityWhite                                         0.001505 ** 
## IncomeLevel>$150k                                      0.181073    
## IncomeLevel$100-150k                                   0.299447    
## IncomeLevel$20-50k                                     0.103095    
## IncomeLevel$50-100k                                    0.000114 ***
## BMI                                                    0.010114 *  
## CESD.10baseline                                        0.015285 *  
## SmokingStatusFormer Smoker                             0.367755    
## SmokingStatusNever Smoked                              0.023831 *  
## SmokingStatusOccasional Smoker                         0.432412    
## RelationshipstatusMarried                              0.750375    
## RelationshipstatusSeparated                            0.634240    
## RelationshipstatusSingle                               0.764487    
## RelationshipstatusWidowed                              0.416002    
## LivingstatusAssisted Living                            0.016090 *  
## LivingstatusHouse                                      0.690858    
## LivingstatusOther                                      0.668932    
## AnxietyYes                                             0.721555    
## MoodDisordYes                                          0.263712    
## Chronicconditions                                      0.108660    
## PASE_TOTALbaseline                                     1.18e-06 ***
## RVLT_Delayed_Normedbaseline                             < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.005487 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_del_adj10trun)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)
## timefactor                    685.4   685.4     1 6709.9   99.1114 < 2.2e-16
## Pandemic                       24.2    24.2     1 6876.0    3.4946 0.0616112
## Age                            25.4    25.4     1 6907.1    3.6716 0.0553885
## Sex                           252.5   252.5     1 6889.7   36.5029 1.604e-09
## Education                      76.8    25.6     3 6925.4    3.6996 0.0112449
## Ethnicity                      69.7    69.7     1 6926.0   10.0806 0.0015049
## IncomeLevel                   131.3    32.8     4 6875.5    4.7477 0.0007976
## BMI                            45.8    45.8     1 6849.4    6.6185 0.0101136
## CESD.10baseline                40.7    40.7     1 6915.2    5.8863 0.0152849
## SmokingStatus                  68.4    22.8     3 6862.0    3.2965 0.0195852
## Relationshipstatus             16.0     4.0     4 6933.4    0.5800 0.6771741
## Livingstatus                   46.2    15.4     3 6916.9    2.2258 0.0830201
## Anxiety                         0.9     0.9     1 6870.2    0.1270 0.7215547
## MoodDisord                      8.6     8.6     1 6877.1    1.2494 0.2637125
## Chronicconditions              17.8    17.8     1 6874.3    2.5743 0.1086599
## PASE_TOTALbaseline            163.5   163.5     1 6889.5   23.6451 1.184e-06
## RVLT_Delayed_Normedbaseline 13008.4 13008.4     1 6894.0 1880.9329 < 2.2e-16
## timefactor:Pandemic            53.4    53.4     1 6710.1    7.7166 0.0054867
##                                
## timefactor                  ***
## Pandemic                    .  
## Age                         .  
## Sex                         ***
## Education                   *  
## Ethnicity                   ** 
## IncomeLevel                 ***
## BMI                         *  
## CESD.10baseline             *  
## SmokingStatus               *  
## Relationshipstatus             
## Livingstatus                .  
## Anxiety                        
## MoodDisord                     
## Chronicconditions              
## PASE_TOTALbaseline          ***
## RVLT_Delayed_Normedbaseline ***
## timefactor:Pandemic         ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.3.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.42374 0.2293047 7146.69  9.974232
##  FU2 data collected before COVID-19 10.67961 0.2324538 7194.02 10.223934
##  upper.CL
##  10.87324
##  11.13529
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  11.00833 0.2294958 7162.63 10.558450
##  FU2 data collected before COVID-19 11.00914 0.2324262 7191.84 10.553518
##  upper.CL
##  11.45821
##  11.46476
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##  -0.25587524 0.08230741 11778.10  -3.109  0.0019
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df t.ratio p.value
##  -0.00081211 0.08287418 11856.76  -0.010  0.9922
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL   upper.CL
##  -0.25587524 0.08230741 11778.10 -0.4172114 -0.0945391
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE       df   lower.CL   upper.CL
##  -0.00081211 0.08287418 11856.76 -0.1632591  0.1616349
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.3.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj10trun <- summary(lsmeans(modelRVLT_del_adj10trun, ~timefactor|Pandemic))
RVLTdelayed_lsmeans_adj10trun$Time<-NA
RVLTdelayed_lsmeans_adj10trun$Time[RVLTdelayed_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj10trun$Time[RVLTdelayed_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

11.3.3) Mental Alteration Test

11.3.3.1) Model

modelMAT_adj10trun<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
                      (1|ID), data= truncated.data_long_2)
summary(modelMAT_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +      (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 66278.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3069 -0.5275 -0.0879  0.3683  4.7473 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.248    1.499   
##  Residual             9.083    3.014   
## Number of obs: 12616, groups:  ID, 6874
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.818e+00  4.569e-01
## timefactor2                                            -1.367e+00  7.348e-02
## PandemicFU2 data collected before COVID-19             -3.648e-01  8.637e-02
## Age                                                    -1.199e-02  4.239e-03
## SexM                                                   -1.227e+00  7.340e-02
## EducationHigh School Diploma                           -3.722e-02  1.031e-01
## EducationLess than High School Diploma                 -1.354e-01  1.492e-01
## EducationSome College                                  -2.331e-02  1.270e-01
## EthnicityWhite                                          9.034e-01  2.063e-01
## IncomeLevel>$150k                                       1.273e-01  1.881e-01
## IncomeLevel$100-150k                                    1.462e-01  1.526e-01
## IncomeLevel$20-50k                                      1.663e-01  1.036e-01
## IncomeLevel$50-100k                                     1.509e-01  1.114e-01
## BMI                                                    -1.323e-02  6.920e-03
## CESD.10baseline                                        -2.175e-02  8.296e-03
## SmokingStatusFormer Smoker                              8.338e-03  1.396e-01
## SmokingStatusNever Smoked                              -8.297e-02  1.452e-01
## SmokingStatusOccasional Smoker                          1.660e-01  2.751e-01
## RelationshipstatusMarried                              -4.425e-03  1.210e-01
## RelationshipstatusSeparated                            -6.076e-02  2.328e-01
## RelationshipstatusSingle                                5.469e-01  1.637e-01
## RelationshipstatusWidowed                              -1.947e-01  1.689e-01
## LivingstatusAssisted Living                            -1.228e-01  5.100e-01
## LivingstatusHouse                                      -1.599e-01  1.106e-01
## LivingstatusOther                                      -1.830e-01  4.123e-01
## AnxietyYes                                              8.310e-02  1.435e-01
## MoodDisordYes                                           1.138e-01  1.035e-01
## Chronicconditions                                      -3.578e-02  1.704e-02
## PASE_TOTALbaseline                                     -5.028e-04  4.859e-04
## MAT_Normedbaseline                                      4.422e-01  9.591e-03
## timefactor2:PandemicFU2 data collected before COVID-19  3.692e-01  1.087e-01
##                                                                df t value
## (Intercept)                                             6.718e+03  17.112
## timefactor2                                             6.458e+03 -18.600
## PandemicFU2 data collected before COVID-19              1.219e+04  -4.224
## Age                                                     6.692e+03  -2.828
## SexM                                                    6.612e+03 -16.714
## EducationHigh School Diploma                            6.615e+03  -0.361
## EducationLess than High School Diploma                  6.833e+03  -0.908
## EducationSome College                                   6.532e+03  -0.184
## EthnicityWhite                                          6.702e+03   4.378
## IncomeLevel>$150k                                       6.589e+03   0.677
## IncomeLevel$100-150k                                    6.576e+03   0.958
## IncomeLevel$20-50k                                      6.669e+03   1.606
## IncomeLevel$50-100k                                     6.629e+03   1.355
## BMI                                                     6.537e+03  -1.912
## CESD.10baseline                                         6.592e+03  -2.621
## SmokingStatusFormer Smoker                              6.546e+03   0.060
## SmokingStatusNever Smoked                               6.549e+03  -0.571
## SmokingStatusOccasional Smoker                          6.411e+03   0.603
## RelationshipstatusMarried                               6.673e+03  -0.037
## RelationshipstatusSeparated                             6.669e+03  -0.261
## RelationshipstatusSingle                                6.649e+03   3.341
## RelationshipstatusWidowed                               6.733e+03  -1.152
## LivingstatusAssisted Living                             6.525e+03  -0.241
## LivingstatusHouse                                       6.683e+03  -1.446
## LivingstatusOther                                       6.658e+03  -0.444
## AnxietyYes                                              6.593e+03   0.579
## MoodDisordYes                                           6.558e+03   1.100
## Chronicconditions                                       6.630e+03  -2.100
## PASE_TOTALbaseline                                      6.563e+03  -1.035
## MAT_Normedbaseline                                      6.662e+03  46.108
## timefactor2:PandemicFU2 data collected before COVID-19  6.404e+03   3.396
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             2.42e-05 ***
## Age                                                    0.004693 ** 
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.718245    
## EducationLess than High School Diploma                 0.364045    
## EducationSome College                                  0.854364    
## EthnicityWhite                                         1.22e-05 ***
## IncomeLevel>$150k                                      0.498705    
## IncomeLevel$100-150k                                   0.338226    
## IncomeLevel$20-50k                                     0.108364    
## IncomeLevel$50-100k                                    0.175549    
## BMI                                                    0.055902 .  
## CESD.10baseline                                        0.008775 ** 
## SmokingStatusFormer Smoker                             0.952379    
## SmokingStatusNever Smoked                              0.567860    
## SmokingStatusOccasional Smoker                         0.546266    
## RelationshipstatusMarried                              0.970818    
## RelationshipstatusSeparated                            0.794072    
## RelationshipstatusSingle                               0.000839 ***
## RelationshipstatusWidowed                              0.249266    
## LivingstatusAssisted Living                            0.809692    
## LivingstatusHouse                                      0.148248    
## LivingstatusOther                                      0.657210    
## AnxietyYes                                             0.562654    
## MoodDisordYes                                          0.271302    
## Chronicconditions                                      0.035794 *  
## PASE_TOTALbaseline                                     0.300789    
## MAT_Normedbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.000689 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelMAT_adj10trun)
## Type III Analysis of Variance Table with Satterthwaite's method
##                      Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)    
## timefactor           4295.2  4295.2     1 6403.4  472.9034 < 2.2e-16 ***
## Pandemic               65.8    65.8     1 6617.1    7.2414 0.0071419 ** 
## Age                    72.7    72.7     1 6692.1    7.9996 0.0046927 ** 
## Sex                  2537.2  2537.2     1 6611.8  279.3479 < 2.2e-16 ***
## Education               8.0     2.7     3 6659.0    0.2930 0.8305184    
## Ethnicity             174.1   174.1     1 6702.4   19.1680 1.215e-05 ***
## IncomeLevel            24.3     6.1     4 6593.9    0.6680 0.6141263    
## BMI                    33.2    33.2     1 6537.4    3.6563 0.0559023 .  
## CESD.10baseline        62.4    62.4     1 6591.5    6.8721 0.0087754 ** 
## SmokingStatus          19.9     6.6     3 6509.9    0.7316 0.5330417    
## Relationshipstatus    202.2    50.5     4 6673.6    5.5644 0.0001809 ***
## Livingstatus           19.1     6.4     3 6620.8    0.7023 0.5505779    
## Anxiety                 3.0     3.0     1 6592.9    0.3352 0.5626543    
## MoodDisord             11.0    11.0     1 6557.9    1.2104 0.2713020    
## Chronicconditions      40.0    40.0     1 6629.8    4.4087 0.0357943 *  
## PASE_TOTALbaseline      9.7     9.7     1 6563.1    1.0709 0.3007890    
## MAT_Normedbaseline  19309.3 19309.3     1 6662.1 2125.9530 < 2.2e-16 ***
## timefactor:Pandemic   104.7   104.7     1 6403.6   11.5295 0.0006892 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.3.3.2) Estimated marginal means

lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.875132 0.2254926 6988.29 10.433098
##  FU2 data collected before COVID-19 10.510338 0.2286060 7042.50 10.062202
##   upper.CL
##  11.317166
##  10.958475
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.508377 0.2251145 6962.76  9.067084
##  FU2 data collected before COVID-19  9.512742 0.2285077 7036.20  9.064798
##   upper.CL
##   9.949670
##   9.960686
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##   0.3647938 0.08637245 12192.81   4.223  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.0043649 0.08613932 12183.62  -0.051  0.9596
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL  upper.CL
##   0.3647938 0.08637245 12192.81  0.1954901 0.5340975
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL  upper.CL
##  -0.0043649 0.08613932 12183.62 -0.1732116 0.1644818
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.3.3.3) Graph of estimated marginal means

MAT_lsmeans_adj10trun <- summary(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor))
MAT_lsmeans_adj10trun$Time<-NA
MAT_lsmeans_adj10trun$Time[MAT_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj10trun$Time[MAT_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

11.3.4) Animal Fluency

11.3.4.1) Model

modelAnimals_adj10trun<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
                           (1|ID), data= truncated.data_long_2)
summary(modelAnimals_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 63557.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8956 -0.5444 -0.0183  0.5284  4.2314 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.970    1.723   
##  Residual             3.839    1.959   
## Number of obs: 13639, groups:  ID, 6978
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             5.717e+00  3.736e-01
## timefactor2                                             7.062e-02  4.581e-02
## PandemicFU2 data collected before COVID-19              1.715e-01  6.422e-02
## Age                                                    -2.231e-02  3.446e-03
## SexM                                                   -7.461e-02  6.008e-02
## EducationHigh School Diploma                            1.535e-01  8.440e-02
## EducationLess than High School Diploma                  4.925e-02  1.196e-01
## EducationSome College                                   1.938e-01  1.046e-01
## EthnicityWhite                                          5.418e-01  1.676e-01
## IncomeLevel>$150k                                      -6.036e-02  1.543e-01
## IncomeLevel$100-150k                                    2.849e-02  1.254e-01
## IncomeLevel$20-50k                                     -1.438e-01  8.441e-02
## IncomeLevel$50-100k                                     2.418e-02  9.082e-02
## BMI                                                    -1.050e-02  5.691e-03
## CESD.10baseline                                        -1.045e-02  6.798e-03
## SmokingStatusFormer Smoker                              2.129e-01  1.149e-01
## SmokingStatusNever Smoked                               1.527e-01  1.195e-01
## SmokingStatusOccasional Smoker                          1.721e-02  2.266e-01
## RelationshipstatusMarried                               3.847e-02  9.892e-02
## RelationshipstatusSeparated                             1.660e-01  1.913e-01
## RelationshipstatusSingle                                3.810e-02  1.340e-01
## RelationshipstatusWidowed                              -1.081e-01  1.371e-01
## LivingstatusAssisted Living                            -1.914e-01  4.172e-01
## LivingstatusHouse                                       1.617e-01  9.020e-02
## LivingstatusOther                                      -2.584e-01  3.340e-01
## AnxietyYes                                              2.434e-02  1.181e-01
## MoodDisordYes                                           6.666e-02  8.523e-02
## Chronicconditions                                      -9.786e-03  1.390e-02
## PASE_TOTALbaseline                                      6.268e-04  3.996e-04
## Animal_Fluency_Normedbaseline                           5.520e-01  7.832e-03
## timefactor2:PandemicFU2 data collected before COVID-19  4.275e-02  6.770e-02
##                                                                df t value
## (Intercept)                                             6.955e+03  15.304
## timefactor2                                             6.849e+03   1.542
## PandemicFU2 data collected before COVID-19              1.137e+04   2.671
## Age                                                     6.904e+03  -6.474
## SexM                                                    6.910e+03  -1.242
## EducationHigh School Diploma                            6.922e+03   1.819
## EducationLess than High School Diploma                  6.994e+03   0.412
## EducationSome College                                   6.867e+03   1.852
## EthnicityWhite                                          6.861e+03   3.233
## IncomeLevel>$150k                                       6.920e+03  -0.391
## IncomeLevel$100-150k                                    6.889e+03   0.227
## IncomeLevel$20-50k                                      6.914e+03  -1.704
## IncomeLevel$50-100k                                     6.914e+03   0.266
## BMI                                                     6.894e+03  -1.845
## CESD.10baseline                                         6.928e+03  -1.537
## SmokingStatusFormer Smoker                              6.921e+03   1.853
## SmokingStatusNever Smoked                               6.920e+03   1.278
## SmokingStatusOccasional Smoker                          6.848e+03   0.076
## RelationshipstatusMarried                               6.946e+03   0.389
## RelationshipstatusSeparated                             6.970e+03   0.868
## RelationshipstatusSingle                                6.945e+03   0.284
## RelationshipstatusWidowed                               6.919e+03  -0.789
## LivingstatusAssisted Living                             6.971e+03  -0.459
## LivingstatusHouse                                       6.927e+03   1.793
## LivingstatusOther                                       6.791e+03  -0.774
## AnxietyYes                                              6.904e+03   0.206
## MoodDisordYes                                           6.920e+03   0.782
## Chronicconditions                                       6.889e+03  -0.704
## PASE_TOTALbaseline                                      6.902e+03   1.569
## Animal_Fluency_Normedbaseline                           6.899e+03  70.479
## timefactor2:PandemicFU2 data collected before COVID-19  6.788e+03   0.632
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             0.12322    
## PandemicFU2 data collected before COVID-19              0.00758 ** 
## Age                                                    1.02e-10 ***
## SexM                                                    0.21429    
## EducationHigh School Diploma                            0.06893 .  
## EducationLess than High School Diploma                  0.68041    
## EducationSome College                                   0.06405 .  
## EthnicityWhite                                          0.00123 ** 
## IncomeLevel>$150k                                       0.69562    
## IncomeLevel$100-150k                                    0.82025    
## IncomeLevel$20-50k                                      0.08851 .  
## IncomeLevel$50-100k                                     0.79002    
## BMI                                                     0.06514 .  
## CESD.10baseline                                         0.12444    
## SmokingStatusFormer Smoker                              0.06391 .  
## SmokingStatusNever Smoked                               0.20136    
## SmokingStatusOccasional Smoker                          0.93946    
## RelationshipstatusMarried                               0.69736    
## RelationshipstatusSeparated                             0.38549    
## RelationshipstatusSingle                                0.77619    
## RelationshipstatusWidowed                               0.43034    
## LivingstatusAssisted Living                             0.64646    
## LivingstatusHouse                                       0.07308 .  
## LivingstatusOther                                       0.43910    
## AnxietyYes                                              0.83668    
## MoodDisordYes                                           0.43419    
## Chronicconditions                                       0.48157    
## PASE_TOTALbaseline                                      0.11675    
## Animal_Fluency_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19  0.52769    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelAnimals_adj10trun)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)
## timefactor                       28.4    28.4     1 6787.8    7.3868 0.0065873
## Pandemic                         47.5    47.5     1 6902.4   12.3643 0.0004404
## Age                             160.9   160.9     1 6903.9   41.9160 1.018e-10
## Sex                               5.9     5.9     1 6910.4    1.5425 0.2142865
## Education                        22.8     7.6     3 6927.9    1.9767 0.1151625
## Ethnicity                        40.1    40.1     1 6860.6   10.4492 0.0012328
## IncomeLevel                      31.2     7.8     4 6907.9    2.0345 0.0867756
## BMI                              13.1    13.1     1 6893.8    3.4026 0.0651363
## CESD.10baseline                   9.1     9.1     1 6928.3    2.3611 0.1244409
## SmokingStatus                    17.4     5.8     3 6885.1    1.5098 0.2097781
## Relationshipstatus                9.5     2.4     4 6941.7    0.6202 0.6480771
## Livingstatus                     20.0     6.7     3 6894.2    1.7398 0.1565247
## Anxiety                           0.2     0.2     1 6903.8    0.0425 0.8366777
## MoodDisord                        2.3     2.3     1 6920.0    0.6117 0.4341865
## Chronicconditions                 1.9     1.9     1 6889.3    0.4954 0.4815735
## PASE_TOTALbaseline                9.4     9.4     1 6901.5    2.4611 0.1167464
## Animal_Fluency_Normedbaseline 19068.7 19068.7     1 6898.9 4967.3284 < 2.2e-16
## timefactor:Pandemic               1.5     1.5     1 6788.0    0.3989 0.5276883
##                                  
## timefactor                    ** 
## Pandemic                      ***
## Age                           ***
## Sex                              
## Education                        
## Ethnicity                     ** 
## IncomeLevel                   .  
## BMI                           .  
## CESD.10baseline                  
## SmokingStatus                    
## Relationshipstatus               
## Livingstatus                     
## Anxiety                          
## MoodDisord                       
## Chronicconditions                
## PASE_TOTALbaseline               
## Animal_Fluency_Normedbaseline ***
## timefactor:Pandemic              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.3.4.2) Estimated marginal means

lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean        SE      df lower.CL
##  FU2 data collected after COVID-19  10.75213 0.1828604 7112.92 10.39367
##  FU2 data collected before COVID-19 10.92364 0.1852591 7147.53 10.56048
##  upper.CL
##  11.11059
##  11.28680
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE      df lower.CL
##  FU2 data collected after COVID-19  10.82274 0.1829427 7121.97 10.46412
##  FU2 data collected before COVID-19 11.03701 0.1853420 7156.57 10.67369
##  upper.CL
##  11.18137
##  11.40034
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.1715117 0.06422463 11375.69  -2.670  0.0076
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df t.ratio p.value
##  -0.2142664 0.06469093 11483.10  -3.312  0.0009
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.1715117 0.06422463 11375.69 -0.2974031 -0.04562033
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE       df   lower.CL    upper.CL
##  -0.2142664 0.06469093 11483.10 -0.3410716 -0.08746112
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.3.4.3) Graph of estimated marginal means

Animals_lsmeans_adj10trun <- summary(lsmeans(modelAnimals_adj10trun, ~timefactor|Pandemic))
Animals_lsmeans_adj10trun$Time<-NA
Animals_lsmeans_adj10trun$Time[Animals_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj10trun$Time[Animals_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

11.4) Age and Sex Interaction Model

All models use normalized cognitive scores. Each model is adjusted for education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

11.4.1) RVLT Immediate Recall

11.4.1.1) Model

modelRVLT_imm_adj11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_imm_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 70675.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6842 -0.5711 -0.0390  0.5248  3.8767 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 3.969    1.992   
##  Residual             7.501    2.739   
## Number of obs: 13535, groups:  ID, 6973
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.419e+00
## timefactor2                                                                4.083e-01
## PandemicFU2 data collected before COVID-19                                 6.392e-02
## Age_sexFemales 65+                                                        -1.020e-01
## Age_sexMales 45-64                                                        -6.870e-01
## Age_sexMales 65+                                                          -7.001e-01
## EducationHigh School Diploma                                               2.785e-01
## EducationLess than High School Diploma                                     4.132e-01
## EducationSome College                                                      1.261e-01
## EthnicityWhite                                                             6.143e-01
## IncomeLevel>$150k                                                          4.011e-01
## IncomeLevel$100-150k                                                       2.917e-01
## IncomeLevel$20-50k                                                         1.548e-01
## IncomeLevel$50-100k                                                        4.087e-01
## BMI                                                                       -1.458e-02
## CESD.10baseline                                                           -2.201e-02
## SmokingStatusFormer Smoker                                                 2.137e-01
## SmokingStatusNever Smoked                                                  3.353e-01
## SmokingStatusOccasional Smoker                                             3.579e-02
## RelationshipstatusMarried                                                  3.378e-01
## RelationshipstatusSeparated                                                3.182e-01
## RelationshipstatusSingle                                                   2.442e-01
## RelationshipstatusWidowed                                                 -1.827e-02
## LivingstatusAssisted Living                                               -9.862e-01
## LivingstatusHouse                                                         -3.390e-02
## LivingstatusOther                                                         -5.054e-01
## AnxietyYes                                                                 6.142e-02
## MoodDisordYes                                                             -1.045e-01
## Chronicconditions                                                         -3.015e-02
## PASE_TOTALbaseline                                                         2.510e-03
## RVLT_Immediate_Normedbaseline                                              3.543e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.805e-01
## timefactor2:Age_sexFemales 65+                                            -4.964e-01
## timefactor2:Age_sexMales 45-64                                             9.972e-02
## timefactor2:Age_sexMales 65+                                              -4.198e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.279e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.283e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                6.785e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  9.935e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.135e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    4.156e-01
##                                                                           Std. Error
## (Intercept)                                                                3.758e-01
## timefactor2                                                                1.204e-01
## PandemicFU2 data collected before COVID-19                                 1.428e-01
## Age_sexFemales 65+                                                         1.897e-01
## Age_sexMales 45-64                                                         1.386e-01
## Age_sexMales 65+                                                           1.761e-01
## EducationHigh School Diploma                                               1.064e-01
## EducationLess than High School Diploma                                     1.514e-01
## EducationSome College                                                      1.319e-01
## EthnicityWhite                                                             2.106e-01
## IncomeLevel>$150k                                                          1.948e-01
## IncomeLevel$100-150k                                                       1.578e-01
## IncomeLevel$20-50k                                                         1.067e-01
## IncomeLevel$50-100k                                                        1.147e-01
## BMI                                                                        7.161e-03
## CESD.10baseline                                                            8.549e-03
## SmokingStatusFormer Smoker                                                 1.449e-01
## SmokingStatusNever Smoked                                                  1.510e-01
## SmokingStatusOccasional Smoker                                             2.852e-01
## RelationshipstatusMarried                                                  1.247e-01
## RelationshipstatusSeparated                                                2.416e-01
## RelationshipstatusSingle                                                   1.689e-01
## RelationshipstatusWidowed                                                  1.729e-01
## LivingstatusAssisted Living                                                5.251e-01
## LivingstatusHouse                                                          1.138e-01
## LivingstatusOther                                                          4.213e-01
## AnxietyYes                                                                 1.486e-01
## MoodDisordYes                                                              1.075e-01
## Chronicconditions                                                          1.740e-02
## PASE_TOTALbaseline                                                         4.884e-04
## RVLT_Immediate_Normedbaseline                                              8.982e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.648e-01
## timefactor2:Age_sexFemales 65+                                             2.090e-01
## timefactor2:Age_sexMales 45-64                                             1.564e-01
## timefactor2:Age_sexMales 65+                                               1.998e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.489e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.073e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.446e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.878e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.387e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.832e-01
##                                                                                   df
## (Intercept)                                                                7.220e+03
## timefactor2                                                                6.860e+03
## PandemicFU2 data collected before COVID-19                                 1.214e+04
## Age_sexFemales 65+                                                         1.165e+04
## Age_sexMales 45-64                                                         1.191e+04
## Age_sexMales 65+                                                           1.190e+04
## EducationHigh School Diploma                                               6.914e+03
## EducationLess than High School Diploma                                     7.040e+03
## EducationSome College                                                      6.834e+03
## EthnicityWhite                                                             6.836e+03
## IncomeLevel>$150k                                                          6.913e+03
## IncomeLevel$100-150k                                                       6.869e+03
## IncomeLevel$20-50k                                                         6.920e+03
## IncomeLevel$50-100k                                                        6.917e+03
## BMI                                                                        6.884e+03
## CESD.10baseline                                                            6.904e+03
## SmokingStatusFormer Smoker                                                 6.933e+03
## SmokingStatusNever Smoked                                                  6.934e+03
## SmokingStatusOccasional Smoker                                             6.804e+03
## RelationshipstatusMarried                                                  6.923e+03
## RelationshipstatusSeparated                                                6.985e+03
## RelationshipstatusSingle                                                   6.923e+03
## RelationshipstatusWidowed                                                  6.927e+03
## LivingstatusAssisted Living                                                6.926e+03
## LivingstatusHouse                                                          6.919e+03
## LivingstatusOther                                                          6.791e+03
## AnxietyYes                                                                 6.889e+03
## MoodDisordYes                                                              6.906e+03
## Chronicconditions                                                          6.906e+03
## PASE_TOTALbaseline                                                         6.885e+03
## RVLT_Immediate_Normedbaseline                                              6.921e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.790e+03
## timefactor2:Age_sexFemales 65+                                             6.854e+03
## timefactor2:Age_sexMales 45-64                                             6.820e+03
## timefactor2:Age_sexMales 65+                                               6.875e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.214e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.215e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.216e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.814e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.749e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.808e+03
##                                                                           t value
## (Intercept)                                                                17.081
## timefactor2                                                                 3.390
## PandemicFU2 data collected before COVID-19                                  0.448
## Age_sexFemales 65+                                                         -0.538
## Age_sexMales 45-64                                                         -4.958
## Age_sexMales 65+                                                           -3.976
## EducationHigh School Diploma                                                2.617
## EducationLess than High School Diploma                                      2.730
## EducationSome College                                                       0.956
## EthnicityWhite                                                              2.917
## IncomeLevel>$150k                                                           2.059
## IncomeLevel$100-150k                                                        1.848
## IncomeLevel$20-50k                                                          1.452
## IncomeLevel$50-100k                                                         3.562
## BMI                                                                        -2.036
## CESD.10baseline                                                            -2.574
## SmokingStatusFormer Smoker                                                  1.474
## SmokingStatusNever Smoked                                                   2.221
## SmokingStatusOccasional Smoker                                              0.126
## RelationshipstatusMarried                                                   2.709
## RelationshipstatusSeparated                                                 1.317
## RelationshipstatusSingle                                                    1.446
## RelationshipstatusWidowed                                                  -0.106
## LivingstatusAssisted Living                                                -1.878
## LivingstatusHouse                                                          -0.298
## LivingstatusOther                                                          -1.200
## AnxietyYes                                                                  0.413
## MoodDisordYes                                                              -0.972
## Chronicconditions                                                          -1.733
## PASE_TOTALbaseline                                                          5.140
## RVLT_Immediate_Normedbaseline                                              39.448
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.096
## timefactor2:Age_sexFemales 65+                                             -2.375
## timefactor2:Age_sexMales 45-64                                              0.638
## timefactor2:Age_sexMales 65+                                               -2.101
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.514
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.619
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.277
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.345
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.894
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.467
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.000703
## PandemicFU2 data collected before COVID-19                                0.654369
## Age_sexFemales 65+                                                        0.590623
## Age_sexMales 45-64                                                        7.21e-07
## Age_sexMales 65+                                                          7.06e-05
## EducationHigh School Diploma                                              0.008883
## EducationLess than High School Diploma                                    0.006347
## EducationSome College                                                     0.339107
## EthnicityWhite                                                            0.003551
## IncomeLevel>$150k                                                         0.039571
## IncomeLevel$100-150k                                                      0.064584
## IncomeLevel$20-50k                                                        0.146668
## IncomeLevel$50-100k                                                       0.000371
## BMI                                                                       0.041772
## CESD.10baseline                                                           0.010075
## SmokingStatusFormer Smoker                                                0.140424
## SmokingStatusNever Smoked                                                 0.026361
## SmokingStatusOccasional Smoker                                            0.900129
## RelationshipstatusMarried                                                 0.006756
## RelationshipstatusSeparated                                               0.187821
## RelationshipstatusSingle                                                  0.148248
## RelationshipstatusWidowed                                                 0.915818
## LivingstatusAssisted Living                                               0.060430
## LivingstatusHouse                                                         0.765724
## LivingstatusOther                                                         0.230252
## AnxietyYes                                                                0.679422
## MoodDisordYes                                                             0.330894
## Chronicconditions                                                         0.083121
## PASE_TOTALbaseline                                                        2.83e-07
## RVLT_Immediate_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.273332
## timefactor2:Age_sexFemales 65+                                            0.017580
## timefactor2:Age_sexMales 45-64                                            0.523815
## timefactor2:Age_sexMales 65+                                              0.035651
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.607355
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.535879
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.781483
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.729926
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.371132
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.142294
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                    ** 
## EducationSome College                                                        
## EthnicityWhite                                                            ** 
## IncomeLevel>$150k                                                         *  
## IncomeLevel$100-150k                                                      .  
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       *  
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 *  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 ** 
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               .  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         .  
## PASE_TOTALbaseline                                                        ***
## RVLT_Immediate_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              *  
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_imm_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)
## timefactor                      121.7   121.7     1 6796.9   16.2244 5.688e-05
## Pandemic                         30.0    30.0     1 6916.2    3.9936  0.045711
## Age_sex                         476.5   158.8     3 6913.7   21.1776 1.189e-13
## Education                        94.2    31.4     3 6929.0    4.1851  0.005731
## Ethnicity                        63.8    63.8     1 6836.0    8.5063  0.003551
## IncomeLevel                     118.4    29.6     4 6891.9    3.9465  0.003344
## BMI                              31.1    31.1     1 6884.2    4.1460  0.041772
## CESD.10baseline                  49.7    49.7     1 6903.6    6.6253  0.010075
## SmokingStatus                    48.3    16.1     3 6865.9    2.1457  0.092277
## Relationshipstatus               89.9    22.5     4 6947.8    2.9951  0.017564
## Livingstatus                     36.2    12.1     3 6876.5    1.6090  0.185016
## Anxiety                           1.3     1.3     1 6889.2    0.1708  0.679422
## MoodDisord                        7.1     7.1     1 6905.6    0.9455  0.330894
## Chronicconditions                22.5    22.5     1 6905.7    3.0037  0.083121
## PASE_TOTALbaseline              198.1   198.1     1 6885.0   26.4159 2.828e-07
## RVLT_Immediate_Normedbaseline 11671.8 11671.8     1 6921.3 1556.1059 < 2.2e-16
## timefactor:Pandemic               0.0     0.0     1 6796.9    0.0003  0.987323
## timefactor:Age_sex              168.6    56.2     3 6789.6    7.4915 5.285e-05
## Pandemic:Age_sex                 20.5     6.8     3 6909.2    0.9116  0.434427
## timefactor:Pandemic:Age_sex      17.5     5.8     3 6789.7    0.7783  0.505891
##                                  
## timefactor                    ***
## Pandemic                      *  
## Age_sex                       ***
## Education                     ** 
## Ethnicity                     ** 
## IncomeLevel                   ** 
## BMI                           *  
## CESD.10baseline               *  
## SmokingStatus                 .  
## Relationshipstatus            *  
## Livingstatus                     
## Anxiety                          
## MoodDisord                       
## Chronicconditions             .  
## PASE_TOTALbaseline            ***
## RVLT_Immediate_Normedbaseline ***
## timefactor:Pandemic              
## timefactor:Age_sex            ***
## Pandemic:Age_sex                 
## timefactor:Pandemic:Age_sex      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.4.1.2) Estimated marginal means

Significantly lower RVLT immediate for males 65+

lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.647131 0.2485004 7683.22 10.160003
##  FU2 data collected before COVID-19 10.711050 0.2479762 7594.06 10.224948
##  upper.CL
##  11.13426
##  11.19715
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  11.055400 0.2492352 7738.69 10.566831
##  FU2 data collected before COVID-19 10.938824 0.2479560 7592.52 10.452761
##  upper.CL
##  11.54397
##  11.42489
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.545094 0.2702762 8283.62 10.015285
##  FU2 data collected before COVID-19 10.736920 0.2710856 8187.77 10.205523
##  upper.CL
##  11.07490
##  11.26832
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.456998 0.2718555 8391.42  9.924094
##  FU2 data collected before COVID-19 10.567679 0.2715221 8217.91 10.035427
##  upper.CL
##  10.98990
##  11.09993
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.960145 0.2406579 7457.87  9.488388
##  FU2 data collected before COVID-19 10.152377 0.2563110 7954.07  9.649940
##  upper.CL
##  10.43190
##  10.65481
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.468137 0.2408710 7474.38  9.995962
##  FU2 data collected before COVID-19 10.693349 0.2562379 7948.79 10.191056
##  upper.CL
##  10.94031
##  11.19564
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.947081 0.2681872 8113.91  9.421366
##  FU2 data collected before COVID-19 10.078848 0.2697437 8260.35  9.550082
##  upper.CL
##  10.47280
##  10.60761
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.935507 0.2699642 8237.52  9.406309
##  FU2 data collected before COVID-19 10.302399 0.2698670 8269.17  9.773392
##  upper.CL
##  10.46471
##  10.83141
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0639181 0.1427665 12134.98  -0.448  0.6544
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   0.1165756 0.1442447 12236.87   0.808  0.4190
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1918261 0.2043726 12125.42  -0.939  0.3479
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1106807 0.2071293 12258.00  -0.534  0.5931
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1922317 0.1508462 12130.94  -1.274  0.2026
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2252127 0.1509000 12134.44  -1.492  0.1356
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1317663 0.1988727 12164.02  -0.663  0.5076
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3668923 0.2012944 12282.26  -1.823  0.0684
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.0639181 0.1427665 12134.98 -0.3437632 0.2159270
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##   0.1165756 0.1442447 12236.87 -0.1661666 0.3993179
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.1918261 0.2043726 12125.42 -0.5924290 0.2087768
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.1106807 0.2071293 12258.00 -0.5166866 0.2953253
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.1922317 0.1508462 12130.94 -0.4879144 0.1034510
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.2252127 0.1509000 12134.44 -0.5210007 0.0705753
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.1317663 0.1988727 12164.02 -0.5215883 0.2580557
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.3668923 0.2012944 12282.26 -0.7614610 0.0276764
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.4.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj11 <- summary(lsmeans(modelRVLT_imm_adj11, ~timefactor|Pandemic|Age_sex))
RVLTimmediate_lsmeans_adj11$Time<-NA
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

11.4.2) RVLT Delayed Recall

11.4.2.1) Model

modelRVLT_del_adj11<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_del_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 69356.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9609 -0.5522 -0.0351  0.5078  3.9656 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.105    2.026   
##  Residual             6.899    2.627   
## Number of obs: 13415, groups:  ID, 6961
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.148e+00
## timefactor2                                                                8.000e-01
## PandemicFU2 data collected before COVID-19                                 1.582e-01
## Age_sexFemales 65+                                                         1.129e-01
## Age_sexMales 45-64                                                        -4.896e-01
## Age_sexMales 65+                                                          -4.101e-01
## EducationHigh School Diploma                                               2.166e-01
## EducationLess than High School Diploma                                     2.815e-01
## EducationSome College                                                      3.141e-01
## EthnicityWhite                                                             6.586e-01
## IncomeLevel>$150k                                                          2.668e-01
## IncomeLevel$100-150k                                                       1.725e-01
## IncomeLevel$20-50k                                                         1.689e-01
## IncomeLevel$50-100k                                                        4.474e-01
## BMI                                                                       -1.715e-02
## CESD.10baseline                                                           -1.956e-02
## SmokingStatusFormer Smoker                                                 1.046e-01
## SmokingStatusNever Smoked                                                  3.164e-01
## SmokingStatusOccasional Smoker                                             2.048e-01
## RelationshipstatusMarried                                                  5.045e-02
## RelationshipstatusSeparated                                               -9.984e-02
## RelationshipstatusSingle                                                  -3.306e-02
## RelationshipstatusWidowed                                                 -1.919e-01
## LivingstatusAssisted Living                                               -1.296e+00
## LivingstatusHouse                                                          5.338e-02
## LivingstatusOther                                                         -1.715e-01
## AnxietyYes                                                                 5.713e-02
## MoodDisordYes                                                             -1.087e-01
## Chronicconditions                                                         -3.436e-02
## PASE_TOTALbaseline                                                         2.659e-03
## RVLT_Delayed_Normedbaseline                                                3.942e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -4.264e-01
## timefactor2:Age_sexFemales 65+                                            -6.196e-01
## timefactor2:Age_sexMales 45-64                                            -8.355e-02
## timefactor2:Age_sexMales 65+                                              -5.885e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.142e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              9.945e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                8.237e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  3.722e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.475e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.678e-01
##                                                                           Std. Error
## (Intercept)                                                                3.753e-01
## timefactor2                                                                1.158e-01
## PandemicFU2 data collected before COVID-19                                 1.401e-01
## Age_sexFemales 65+                                                         1.865e-01
## Age_sexMales 45-64                                                         1.363e-01
## Age_sexMales 65+                                                           1.738e-01
## EducationHigh School Diploma                                               1.056e-01
## EducationLess than High School Diploma                                     1.507e-01
## EducationSome College                                                      1.306e-01
## EthnicityWhite                                                             2.103e-01
## IncomeLevel>$150k                                                          1.930e-01
## IncomeLevel$100-150k                                                       1.565e-01
## IncomeLevel$20-50k                                                         1.059e-01
## IncomeLevel$50-100k                                                        1.138e-01
## BMI                                                                        7.097e-03
## CESD.10baseline                                                            8.490e-03
## SmokingStatusFormer Smoker                                                 1.435e-01
## SmokingStatusNever Smoked                                                  1.495e-01
## SmokingStatusOccasional Smoker                                             2.832e-01
## RelationshipstatusMarried                                                  1.239e-01
## RelationshipstatusSeparated                                                2.399e-01
## RelationshipstatusSingle                                                   1.675e-01
## RelationshipstatusWidowed                                                  1.717e-01
## LivingstatusAssisted Living                                                5.220e-01
## LivingstatusHouse                                                          1.130e-01
## LivingstatusOther                                                          4.194e-01
## AnxietyYes                                                                 1.475e-01
## MoodDisordYes                                                              1.065e-01
## Chronicconditions                                                          1.725e-02
## PASE_TOTALbaseline                                                         4.848e-04
## RVLT_Delayed_Normedbaseline                                                9.104e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.584e-01
## timefactor2:Age_sexFemales 65+                                             2.021e-01
## timefactor2:Age_sexMales 45-64                                             1.506e-01
## timefactor2:Age_sexMales 65+                                               1.935e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.445e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.036e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.411e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.779e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.298e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.740e-01
##                                                                                   df
## (Intercept)                                                                7.230e+03
## timefactor2                                                                6.751e+03
## PandemicFU2 data collected before COVID-19                                 1.187e+04
## Age_sexFemales 65+                                                         1.137e+04
## Age_sexMales 45-64                                                         1.167e+04
## Age_sexMales 65+                                                           1.169e+04
## EducationHigh School Diploma                                               6.899e+03
## EducationLess than High School Diploma                                     7.063e+03
## EducationSome College                                                      6.797e+03
## EthnicityWhite                                                             6.920e+03
## IncomeLevel>$150k                                                          6.885e+03
## IncomeLevel$100-150k                                                       6.852e+03
## IncomeLevel$20-50k                                                         6.911e+03
## IncomeLevel$50-100k                                                        6.894e+03
## BMI                                                                        6.844e+03
## CESD.10baseline                                                            6.906e+03
## SmokingStatusFormer Smoker                                                 6.898e+03
## SmokingStatusNever Smoked                                                  6.899e+03
## SmokingStatusOccasional Smoker                                             6.825e+03
## RelationshipstatusMarried                                                  6.886e+03
## RelationshipstatusSeparated                                                6.994e+03
## RelationshipstatusSingle                                                   6.878e+03
## RelationshipstatusWidowed                                                  6.904e+03
## LivingstatusAssisted Living                                                6.965e+03
## LivingstatusHouse                                                          6.916e+03
## LivingstatusOther                                                          6.852e+03
## AnxietyYes                                                                 6.865e+03
## MoodDisordYes                                                              6.871e+03
## Chronicconditions                                                          6.872e+03
## PASE_TOTALbaseline                                                         6.882e+03
## RVLT_Delayed_Normedbaseline                                                6.888e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.685e+03
## timefactor2:Age_sexFemales 65+                                             6.798e+03
## timefactor2:Age_sexMales 45-64                                             6.731e+03
## timefactor2:Age_sexMales 65+                                               6.837e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.188e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.189e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.194e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.749e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.661e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.760e+03
##                                                                           t value
## (Intercept)                                                                16.381
## timefactor2                                                                 6.908
## PandemicFU2 data collected before COVID-19                                  1.129
## Age_sexFemales 65+                                                          0.605
## Age_sexMales 45-64                                                         -3.592
## Age_sexMales 65+                                                           -2.360
## EducationHigh School Diploma                                                2.051
## EducationLess than High School Diploma                                      1.868
## EducationSome College                                                       2.404
## EthnicityWhite                                                              3.131
## IncomeLevel>$150k                                                           1.382
## IncomeLevel$100-150k                                                        1.102
## IncomeLevel$20-50k                                                          1.595
## IncomeLevel$50-100k                                                         3.931
## BMI                                                                        -2.416
## CESD.10baseline                                                            -2.304
## SmokingStatusFormer Smoker                                                  0.729
## SmokingStatusNever Smoked                                                   2.117
## SmokingStatusOccasional Smoker                                              0.723
## RelationshipstatusMarried                                                   0.407
## RelationshipstatusSeparated                                                -0.416
## RelationshipstatusSingle                                                   -0.197
## RelationshipstatusWidowed                                                  -1.118
## LivingstatusAssisted Living                                                -2.483
## LivingstatusHouse                                                           0.472
## LivingstatusOther                                                          -0.409
## AnxietyYes                                                                  0.387
## MoodDisordYes                                                              -1.021
## Chronicconditions                                                          -1.992
## PASE_TOTALbaseline                                                          5.484
## RVLT_Delayed_Normedbaseline                                                43.300
## timefactor2:PandemicFU2 data collected before COVID-19                     -2.692
## timefactor2:Age_sexFemales 65+                                             -3.065
## timefactor2:Age_sexMales 45-64                                             -0.555
## timefactor2:Age_sexMales 65+                                               -3.041
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.876
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.489
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.342
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.339
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.077
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.342
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               5.37e-12
## PandemicFU2 data collected before COVID-19                                 0.25897
## Age_sexFemales 65+                                                         0.54491
## Age_sexMales 45-64                                                         0.00033
## Age_sexMales 65+                                                           0.01828
## EducationHigh School Diploma                                               0.04030
## EducationLess than High School Diploma                                     0.06180
## EducationSome College                                                      0.01624
## EthnicityWhite                                                             0.00175
## IncomeLevel>$150k                                                          0.16691
## IncomeLevel$100-150k                                                       0.27060
## IncomeLevel$20-50k                                                         0.11068
## IncomeLevel$50-100k                                                       8.55e-05
## BMI                                                                        0.01572
## CESD.10baseline                                                            0.02124
## SmokingStatusFormer Smoker                                                 0.46600
## SmokingStatusNever Smoked                                                  0.03432
## SmokingStatusOccasional Smoker                                             0.46959
## RelationshipstatusMarried                                                  0.68378
## RelationshipstatusSeparated                                                0.67724
## RelationshipstatusSingle                                                   0.84355
## RelationshipstatusWidowed                                                  0.26371
## LivingstatusAssisted Living                                                0.01304
## LivingstatusHouse                                                          0.63660
## LivingstatusOther                                                          0.68268
## AnxietyYes                                                                 0.69850
## MoodDisordYes                                                              0.30728
## Chronicconditions                                                          0.04644
## PASE_TOTALbaseline                                                        4.31e-08
## RVLT_Delayed_Normedbaseline                                                < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.00712
## timefactor2:Age_sexFemales 65+                                             0.00218
## timefactor2:Age_sexMales 45-64                                             0.57920
## timefactor2:Age_sexMales 65+                                               0.00237
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.38102
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.62520
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.73264
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.18053
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.28139
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.17961
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          *  
## EducationHigh School Diploma                                              *  
## EducationLess than High School Diploma                                    .  
## EducationSome College                                                     *  
## EthnicityWhite                                                            ** 
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       *  
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 *  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               *  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                        ***
## RVLT_Delayed_Normedbaseline                                               ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                            ** 
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ** 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelRVLT_del_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)
## timefactor                    427.4   427.4     1 6745.1   61.9557 4.065e-15
## Pandemic                       36.9    36.9     1 6910.2    5.3524 0.0207231
## Age_sex                       304.0   101.3     3 6900.0   14.6886 1.563e-09
## Education                      73.8    24.6     3 6920.1    3.5677 0.0134906
## Ethnicity                      67.6    67.6     1 6920.5    9.8042 0.0017484
## IncomeLevel                   136.8    34.2     4 6869.5    4.9575 0.0005459
## BMI                            40.3    40.3     1 6844.4    5.8369 0.0157193
## CESD.10baseline                36.6    36.6     1 6905.9    5.3091 0.0212433
## SmokingStatus                  66.7    22.2     3 6857.8    3.2219 0.0216924
## Relationshipstatus             24.4     6.1     4 6929.5    0.8835 0.4727568
## Livingstatus                   49.6    16.5     3 6911.4    2.3974 0.0661089
## Anxiety                         1.0     1.0     1 6864.6    0.1501 0.6984975
## MoodDisord                      7.2     7.2     1 6871.3    1.0425 0.3072815
## Chronicconditions              27.4    27.4     1 6872.4    3.9671 0.0464373
## PASE_TOTALbaseline            207.4   207.4     1 6881.6   30.0708 4.313e-08
## RVLT_Delayed_Normedbaseline 12934.2 12934.2     1 6887.9 1874.9163 < 2.2e-16
## timefactor:Pandemic            22.9    22.9     1 6745.2    3.3266 0.0682102
## timefactor:Age_sex            137.8    45.9     3 6731.8    6.6577 0.0001742
## Pandemic:Age_sex               31.4    10.5     3 6895.5    1.5174 0.2077854
## timefactor:Pandemic:Age_sex    19.4     6.5     3 6731.8    0.9353 0.4225709
##                                
## timefactor                  ***
## Pandemic                    *  
## Age_sex                     ***
## Education                   *  
## Ethnicity                   ** 
## IncomeLevel                 ***
## BMI                         *  
## CESD.10baseline             *  
## SmokingStatus               *  
## Relationshipstatus             
## Livingstatus                .  
## Anxiety                        
## MoodDisord                     
## Chronicconditions           *  
## PASE_TOTALbaseline          ***
## RVLT_Delayed_Normedbaseline ***
## timefactor:Pandemic         .  
## timefactor:Age_sex          ***
## Pandemic:Age_sex               
## timefactor:Pandemic:Age_sex    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.4.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.63930 0.2467907 7669.05 10.155518
##  FU2 data collected before COVID-19 10.79750 0.2460802 7571.16 10.315111
##  upper.CL
##  11.12307
##  11.27988
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  11.43926 0.2472132 7701.66 10.954654
##  FU2 data collected before COVID-19 11.17108 0.2461357 7575.56 10.688586
##  upper.CL
##  11.92386
##  11.65358
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.75218 0.2677513 8209.32 10.227324
##  FU2 data collected before COVID-19 11.12462 0.2688605 8130.15 10.597581
##  upper.CL
##  11.27704
##  11.65165
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.93256 0.2703304 8390.52 10.402650
##  FU2 data collected before COVID-19 11.25081 0.2692896 8160.50 10.722939
##  upper.CL
##  11.46248
##  11.77869
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.14967 0.2390866 7463.82  9.680994
##  FU2 data collected before COVID-19 10.40732 0.2541987 7913.47  9.909028
##  upper.CL
##  10.61835
##  10.90562
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.86609 0.2391318 7467.44 10.397322
##  FU2 data collected before COVID-19 10.94491 0.2542065 7913.96 10.446596
##  upper.CL
##  11.33485
##  11.44322
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.22915 0.2662758 8087.83  9.707179
##  FU2 data collected before COVID-19 10.46972 0.2679598 8238.67  9.944450
##  upper.CL
##  10.75112
##  10.99499
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.44062 0.2679359 8206.12  9.915394
##  FU2 data collected before COVID-19 10.62261 0.2676533 8217.52 10.097940
##  upper.CL
##  10.96584
##  11.14728
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1582006 0.1401408 11868.83  -1.129  0.2590
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   0.2681783 0.1413321 11961.92   1.898  0.0578
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3724328 0.2008426 11867.66  -1.854  0.0637
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3182511 0.2043301 12053.76  -1.558  0.1194
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2576531 0.1482223 11874.09  -1.738  0.0822
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0788205 0.1482680 11877.42  -0.532  0.5950
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2405706 0.1964731 11961.23  -1.224  0.2208
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1819915 0.1984017 12065.07  -0.917  0.3590
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.1582006 0.1401408 11868.83 -0.4328996 0.1164984
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##   0.2681783 0.1413321 11961.92 -0.0088555 0.5452121
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.3724328 0.2008426 11867.66 -0.7661172 0.0212516
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.3182511 0.2043301 12053.76 -0.7187709 0.0822687
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.2576531 0.1482223 11874.09 -0.5481930 0.0328869
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.0788205 0.1482680 11877.42 -0.3694500 0.2118090
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.2405706 0.1964731 11961.23 -0.6256898 0.1445486
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.1819915 0.1984017 12065.07 -0.5708908 0.2069077
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.4.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj11 <- summary(lsmeans(modelRVLT_del_adj11, ~timefactor|Pandemic|Age_sex))
RVLTdelayed_lsmeans_adj11$Time<-NA
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

11.4.3) Mental Alteration Test

11.4.3.1) Model

modelMAT_adj11<- lmer(MAT_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
                      (1|ID), data= truncated.data_long_2)
summary(modelMAT_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + MAT_Normedbaseline + (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 65712.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2923 -0.5209 -0.0531  0.3969  4.4907 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.583    1.607   
##  Residual             8.337    2.887   
## Number of obs: 12616, groups:  ID, 6874
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.347e+00
## timefactor2                                                               -3.248e+00
## PandemicFU2 data collected before COVID-19                                -1.257e+00
## Age_sexFemales 65+                                                        -6.694e-01
## Age_sexMales 45-64                                                        -3.233e+00
## Age_sexMales 65+                                                          -3.301e+00
## EducationHigh School Diploma                                              -5.268e-02
## EducationLess than High School Diploma                                    -1.556e-01
## EducationSome College                                                     -4.562e-02
## EthnicityWhite                                                             8.511e-01
## IncomeLevel>$150k                                                          1.278e-01
## IncomeLevel$100-150k                                                       1.582e-01
## IncomeLevel$20-50k                                                         1.493e-01
## IncomeLevel$50-100k                                                        1.341e-01
## BMI                                                                       -1.124e-02
## CESD.10baseline                                                           -2.172e-02
## SmokingStatusFormer Smoker                                                 1.522e-02
## SmokingStatusNever Smoked                                                 -6.242e-02
## SmokingStatusOccasional Smoker                                             1.890e-01
## RelationshipstatusMarried                                                 -2.531e-02
## RelationshipstatusSeparated                                               -8.381e-02
## RelationshipstatusSingle                                                   5.368e-01
## RelationshipstatusWidowed                                                 -2.280e-01
## LivingstatusAssisted Living                                               -1.794e-01
## LivingstatusHouse                                                         -1.703e-01
## LivingstatusOther                                                         -2.286e-01
## AnxietyYes                                                                 7.590e-02
## MoodDisordYes                                                              1.099e-01
## Chronicconditions                                                         -3.439e-02
## PASE_TOTALbaseline                                                        -3.153e-04
## MAT_Normedbaseline                                                         4.417e-01
## timefactor2:PandemicFU2 data collected before COVID-19                     1.418e+00
## timefactor2:Age_sexFemales 65+                                             4.689e-01
## timefactor2:Age_sexMales 45-64                                             3.291e+00
## timefactor2:Age_sexMales 65+                                               2.884e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              3.934e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.309e+00
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.557e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.594e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.480e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -1.321e+00
##                                                                           Std. Error
## (Intercept)                                                                3.681e-01
## timefactor2                                                                1.299e-01
## PandemicFU2 data collected before COVID-19                                 1.429e-01
## Age_sexFemales 65+                                                         1.934e-01
## Age_sexMales 45-64                                                         1.388e-01
## Age_sexMales 65+                                                           1.799e-01
## EducationHigh School Diploma                                               1.027e-01
## EducationLess than High School Diploma                                     1.486e-01
## EducationSome College                                                      1.264e-01
## EthnicityWhite                                                             2.052e-01
## IncomeLevel>$150k                                                          1.873e-01
## IncomeLevel$100-150k                                                       1.519e-01
## IncomeLevel$20-50k                                                         1.033e-01
## IncomeLevel$50-100k                                                        1.110e-01
## BMI                                                                        6.878e-03
## CESD.10baseline                                                            8.249e-03
## SmokingStatusFormer Smoker                                                 1.389e-01
## SmokingStatusNever Smoked                                                  1.447e-01
## SmokingStatusOccasional Smoker                                             2.741e-01
## RelationshipstatusMarried                                                  1.205e-01
## RelationshipstatusSeparated                                                2.317e-01
## RelationshipstatusSingle                                                   1.629e-01
## RelationshipstatusWidowed                                                  1.680e-01
## LivingstatusAssisted Living                                                5.081e-01
## LivingstatusHouse                                                          1.102e-01
## LivingstatusOther                                                          4.105e-01
## AnxietyYes                                                                 1.428e-01
## MoodDisordYes                                                              1.031e-01
## Chronicconditions                                                          1.682e-02
## PASE_TOTALbaseline                                                         4.690e-04
## MAT_Normedbaseline                                                         9.548e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.779e-01
## timefactor2:Age_sexFemales 65+                                             2.328e-01
## timefactor2:Age_sexMales 45-64                                             1.694e-01
## timefactor2:Age_sexMales 65+                                               2.231e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.541e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.089e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.503e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  3.187e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.596e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.147e-01
##                                                                                   df
## (Intercept)                                                                7.098e+03
## timefactor2                                                                6.317e+03
## PandemicFU2 data collected before COVID-19                                 1.208e+04
## Age_sexFemales 65+                                                         1.183e+04
## Age_sexMales 45-64                                                         1.192e+04
## Age_sexMales 65+                                                           1.195e+04
## EducationHigh School Diploma                                               6.637e+03
## EducationLess than High School Diploma                                     6.853e+03
## EducationSome College                                                      6.555e+03
## EthnicityWhite                                                             6.723e+03
## IncomeLevel>$150k                                                          6.613e+03
## IncomeLevel$100-150k                                                       6.602e+03
## IncomeLevel$20-50k                                                         6.689e+03
## IncomeLevel$50-100k                                                        6.649e+03
## BMI                                                                        6.570e+03
## CESD.10baseline                                                            6.615e+03
## SmokingStatusFormer Smoker                                                 6.575e+03
## SmokingStatusNever Smoked                                                  6.578e+03
## SmokingStatusOccasional Smoker                                             6.441e+03
## RelationshipstatusMarried                                                  6.699e+03
## RelationshipstatusSeparated                                                6.693e+03
## RelationshipstatusSingle                                                   6.672e+03
## RelationshipstatusWidowed                                                  6.758e+03
## LivingstatusAssisted Living                                                6.556e+03
## LivingstatusHouse                                                          6.708e+03
## LivingstatusOther                                                          6.680e+03
## AnxietyYes                                                                 6.615e+03
## MoodDisordYes                                                              6.583e+03
## Chronicconditions                                                          6.654e+03
## PASE_TOTALbaseline                                                         6.589e+03
## MAT_Normedbaseline                                                         6.684e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.249e+03
## timefactor2:Age_sexFemales 65+                                             6.637e+03
## timefactor2:Age_sexMales 45-64                                             6.312e+03
## timefactor2:Age_sexMales 65+                                               6.597e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.214e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.211e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.214e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.541e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.290e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.501e+03
##                                                                           t value
## (Intercept)                                                                22.678
## timefactor2                                                               -25.001
## PandemicFU2 data collected before COVID-19                                 -8.795
## Age_sexFemales 65+                                                         -3.462
## Age_sexMales 45-64                                                        -23.300
## Age_sexMales 65+                                                          -18.352
## EducationHigh School Diploma                                               -0.513
## EducationLess than High School Diploma                                     -1.047
## EducationSome College                                                      -0.361
## EthnicityWhite                                                              4.147
## IncomeLevel>$150k                                                           0.682
## IncomeLevel$100-150k                                                        1.041
## IncomeLevel$20-50k                                                          1.446
## IncomeLevel$50-100k                                                         1.208
## BMI                                                                        -1.634
## CESD.10baseline                                                            -2.634
## SmokingStatusFormer Smoker                                                  0.110
## SmokingStatusNever Smoked                                                  -0.431
## SmokingStatusOccasional Smoker                                              0.690
## RelationshipstatusMarried                                                  -0.210
## RelationshipstatusSeparated                                                -0.362
## RelationshipstatusSingle                                                    3.296
## RelationshipstatusWidowed                                                  -1.358
## LivingstatusAssisted Living                                                -0.353
## LivingstatusHouse                                                          -1.546
## LivingstatusOther                                                          -0.557
## AnxietyYes                                                                  0.532
## MoodDisordYes                                                               1.066
## Chronicconditions                                                          -2.045
## PASE_TOTALbaseline                                                         -0.672
## MAT_Normedbaseline                                                         46.264
## timefactor2:PandemicFU2 data collected before COVID-19                      7.974
## timefactor2:Age_sexFemales 65+                                              2.014
## timefactor2:Age_sexMales 45-64                                             19.426
## timefactor2:Age_sexMales 65+                                               12.930
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.155
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               6.268
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 6.221
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.500
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -5.699
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -4.198
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                 < 2e-16
## Age_sexFemales 65+                                                        0.000539
## Age_sexMales 45-64                                                         < 2e-16
## Age_sexMales 65+                                                           < 2e-16
## EducationHigh School Diploma                                              0.608106
## EducationLess than High School Diploma                                    0.295088
## EducationSome College                                                     0.718135
## EthnicityWhite                                                            3.41e-05
## IncomeLevel>$150k                                                         0.495147
## IncomeLevel$100-150k                                                      0.297709
## IncomeLevel$20-50k                                                        0.148176
## IncomeLevel$50-100k                                                       0.227092
## BMI                                                                       0.102390
## CESD.10baseline                                                           0.008468
## SmokingStatusFormer Smoker                                                0.912753
## SmokingStatusNever Smoked                                                 0.666243
## SmokingStatusOccasional Smoker                                            0.490462
## RelationshipstatusMarried                                                 0.833712
## RelationshipstatusSeparated                                               0.717577
## RelationshipstatusSingle                                                  0.000986
## RelationshipstatusWidowed                                                 0.174660
## LivingstatusAssisted Living                                               0.724074
## LivingstatusHouse                                                         0.122258
## LivingstatusOther                                                         0.577619
## AnxietyYes                                                                0.595041
## MoodDisordYes                                                             0.286426
## Chronicconditions                                                         0.040894
## PASE_TOTALbaseline                                                        0.501426
## MAT_Normedbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    1.82e-15
## timefactor2:Age_sexFemales 65+                                            0.044036
## timefactor2:Age_sexMales 45-64                                             < 2e-16
## timefactor2:Age_sexMales 65+                                               < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.876985
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             3.78e-10
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               5.10e-10
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.616990
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.26e-08
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   2.73e-05
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                          
## BMI                                                                          
## CESD.10baseline                                                           ** 
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                  ***
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                           
## MAT_Normedbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor2:Age_sexMales 45-64                                            ***
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             ***
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelMAT_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)
## timefactor                   3800.4  3800.4     1 6529.5  455.8778 < 2.2e-16
## Pandemic                       38.1    38.1     1 6756.9    4.5645  0.032677
## Age_sex                      2597.7   865.9     3 6720.7  103.8674 < 2.2e-16
## Education                      10.6     3.5     3 6681.6    0.4226  0.736801
## Ethnicity                     143.4   143.4     1 6723.1   17.1975 3.410e-05
## IncomeLevel                    18.4     4.6     4 6616.9    0.5516  0.697846
## BMI                            22.2    22.2     1 6570.2    2.6687  0.102390
## CESD.10baseline                57.8    57.8     1 6614.7    6.9359  0.008468
## SmokingStatus                  15.2     5.1     3 6538.3    0.6085  0.609395
## Relationshipstatus            197.7    49.4     4 6699.3    5.9291 9.267e-05
## Livingstatus                   20.4     6.8     3 6647.2    0.8160  0.484797
## Anxiety                         2.4     2.4     1 6614.7    0.2826  0.595041
## MoodDisord                      9.5     9.5     1 6583.2    1.1365  0.286426
## Chronicconditions              34.9    34.9     1 6653.9    4.1820  0.040894
## PASE_TOTALbaseline              3.8     3.8     1 6589.3    0.4520  0.501426
## MAT_Normedbaseline          17843.0 17843.0     1 6683.8 2140.3430 < 2.2e-16
## timefactor:Pandemic           374.5   374.5     1 6529.4   44.9210 2.223e-11
## timefactor:Age_sex           3928.8  1309.6     3 6486.5  157.0937 < 2.2e-16
## Pandemic:Age_sex              223.8    74.6     3 6724.3    8.9467 6.519e-06
## timefactor:Pandemic:Age_sex   405.5   135.2     3 6486.7   16.2150 1.694e-10
##                                
## timefactor                  ***
## Pandemic                    *  
## Age_sex                     ***
## Education                      
## Ethnicity                   ***
## IncomeLevel                    
## BMI                            
## CESD.10baseline             ** 
## SmokingStatus                  
## Relationshipstatus          ***
## Livingstatus                   
## Anxiety                        
## MoodDisord                     
## Chronicconditions           *  
## PASE_TOTALbaseline             
## MAT_Normedbaseline          ***
## timefactor:Pandemic         ***
## timefactor:Age_sex          ***
## Pandemic:Age_sex            ***
## timefactor:Pandemic:Age_sex ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.4.3.2) Estimated marginal means

lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  12.744170 0.2424164 7626.24 12.268967
##  FU2 data collected before COVID-19 11.487398 0.2416060 7519.67 11.013783
##   upper.CL
##  13.219372
##  11.961013
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.496412 0.2425098 7631.79  9.021026
##  FU2 data collected before COVID-19  9.658130 0.2411439 7489.38  9.185421
##   upper.CL
##   9.971798
##  10.130840
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  12.074787 0.2675305 8484.05 11.550362
##  FU2 data collected before COVID-19 10.857355 0.2664651 8271.71 10.335016
##   upper.CL
##  12.599212
##  11.379693
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.295957 0.2677278 8498.01  8.771145
##  FU2 data collected before COVID-19  9.656410 0.2664173 8268.91  9.134165
##   upper.CL
##   9.820768
##  10.178655
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.511111 0.2344522 7374.37  9.051518
##  FU2 data collected before COVID-19  9.563801 0.2512360 8001.72  9.071313
##   upper.CL
##   9.970704
##  10.056289
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.554576 0.2340652 7347.58  9.095741
##  FU2 data collected before COVID-19  9.546113 0.2501571 7933.92  9.055739
##   upper.CL
##  10.013411
##  10.036487
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.443127 0.2654032 8246.50  8.922870
##  FU2 data collected before COVID-19  9.743585 0.2667713 8408.56  9.220648
##   upper.CL
##   9.963384
##  10.266523
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19   9.079691 0.2675447 8373.78  8.555237
##  FU2 data collected before COVID-19  9.477504 0.2657881 8351.89  8.956493
##   upper.CL
##   9.604145
##   9.998514
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   1.2567714 0.1429002 12082.92   8.795  <.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1617181 0.1430042 12086.02  -1.131  0.2581
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   1.2174328 0.2105332 12165.74   5.783  <.0001
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3604533 0.2116760 12185.95  -1.703  0.0886
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0526895 0.1527851 12137.90  -0.345  0.7302
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   0.0084627 0.1505766 12076.43   0.056  0.9552
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3004582 0.2057341 12172.64  -1.460  0.1442
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3978132 0.2075796 12204.89  -1.916  0.0553
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##   1.2567714 0.1429002 12082.92  0.9766642 1.5368787
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.1617181 0.1430042 12086.02 -0.4420293 0.1185930
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##   1.2174328 0.2105332 12165.74  0.8047542 1.6301114
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.3604533 0.2116760 12185.95 -0.7753718 0.0544653
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.0526895 0.1527851 12137.90 -0.3521728 0.2467937
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##   0.0084627 0.1505766 12076.43 -0.2866916 0.3036170
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.3004582 0.2057341 12172.64 -0.7037296 0.1028132
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL  upper.CL
##  -0.3978132 0.2075796 12204.89 -0.8047020 0.0090757
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.4.3.3) Graph of estimated marginal means

MAT_lsmeans_adj11 <- summary(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex))
MAT_lsmeans_adj11$Time<-NA
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

11.4.4) Animal Fluency

11.4.4.1) Model

modelAnimals_adj11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
                           (1|ID), data= truncated.data_long_2)
summary(modelAnimals_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 63554.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8785 -0.5442 -0.0150  0.5311  4.2941 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.980    1.726   
##  Residual             3.829    1.957   
## Number of obs: 13639, groups:  ID, 6978
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                4.345e+00
## timefactor2                                                                2.865e-01
## PandemicFU2 data collected before COVID-19                                 2.752e-01
## Age_sexFemales 65+                                                        -3.597e-01
## Age_sexMales 45-64                                                         3.488e-02
## Age_sexMales 65+                                                          -1.890e-01
## EducationHigh School Diploma                                               1.514e-01
## EducationLess than High School Diploma                                     4.088e-02
## EducationSome College                                                      1.736e-01
## EthnicityWhite                                                             5.188e-01
## IncomeLevel>$150k                                                         -4.856e-02
## IncomeLevel$100-150k                                                       4.740e-02
## IncomeLevel$20-50k                                                        -1.525e-01
## IncomeLevel$50-100k                                                        2.300e-02
## BMI                                                                       -8.945e-03
## CESD.10baseline                                                           -9.496e-03
## SmokingStatusFormer Smoker                                                 1.973e-01
## SmokingStatusNever Smoked                                                  1.470e-01
## SmokingStatusOccasional Smoker                                             1.706e-02
## RelationshipstatusMarried                                                  4.526e-02
## RelationshipstatusSeparated                                                1.833e-01
## RelationshipstatusSingle                                                   6.193e-02
## RelationshipstatusWidowed                                                 -1.378e-01
## LivingstatusAssisted Living                                               -2.351e-01
## LivingstatusHouse                                                          1.614e-01
## LivingstatusOther                                                         -2.425e-01
## AnxietyYes                                                                 4.923e-02
## MoodDisordYes                                                              6.684e-02
## Chronicconditions                                                         -1.520e-02
## PASE_TOTALbaseline                                                         9.840e-04
## Animal_Fluency_Normedbaseline                                              5.512e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.048e-01
## timefactor2:Age_sexFemales 65+                                            -3.209e-01
## timefactor2:Age_sexMales 45-64                                            -2.013e-01
## timefactor2:Age_sexMales 65+                                              -5.359e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.223e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -3.626e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -1.818e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -6.303e-03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  3.109e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.489e-01
##                                                                           Std. Error
## (Intercept)                                                                3.005e-01
## timefactor2                                                                8.587e-02
## PandemicFU2 data collected before COVID-19                                 1.097e-01
## Age_sexFemales 65+                                                         1.459e-01
## Age_sexMales 45-64                                                         1.066e-01
## Age_sexMales 65+                                                           1.354e-01
## EducationHigh School Diploma                                               8.448e-02
## EducationLess than High School Diploma                                     1.197e-01
## EducationSome College                                                      1.046e-01
## EthnicityWhite                                                             1.676e-01
## IncomeLevel>$150k                                                          1.544e-01
## IncomeLevel$100-150k                                                       1.254e-01
## IncomeLevel$20-50k                                                         8.459e-02
## IncomeLevel$50-100k                                                        9.100e-02
## BMI                                                                        5.681e-03
## CESD.10baseline                                                            6.793e-03
## SmokingStatusFormer Smoker                                                 1.149e-01
## SmokingStatusNever Smoked                                                  1.196e-01
## SmokingStatusOccasional Smoker                                             2.268e-01
## RelationshipstatusMarried                                                  9.909e-02
## RelationshipstatusSeparated                                                1.914e-01
## RelationshipstatusSingle                                                   1.340e-01
## RelationshipstatusWidowed                                                  1.370e-01
## LivingstatusAssisted Living                                                4.175e-01
## LivingstatusHouse                                                          9.035e-02
## LivingstatusOther                                                          3.343e-01
## AnxietyYes                                                                 1.180e-01
## MoodDisordYes                                                              8.535e-02
## Chronicconditions                                                          1.379e-02
## PASE_TOTALbaseline                                                         3.875e-04
## Animal_Fluency_Normedbaseline                                              7.839e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.173e-01
## timefactor2:Age_sexFemales 65+                                             1.487e-01
## timefactor2:Age_sexMales 45-64                                             1.116e-01
## timefactor2:Age_sexMales 65+                                               1.422e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.907e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.593e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.875e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.041e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.702e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.013e-01
##                                                                                   df
## (Intercept)                                                                7.170e+03
## timefactor2                                                                6.869e+03
## PandemicFU2 data collected before COVID-19                                 1.150e+04
## Age_sexFemales 65+                                                         1.098e+04
## Age_sexMales 45-64                                                         1.129e+04
## Age_sexMales 65+                                                           1.124e+04
## EducationHigh School Diploma                                               6.919e+03
## EducationLess than High School Diploma                                     6.993e+03
## EducationSome College                                                      6.863e+03
## EthnicityWhite                                                             6.857e+03
## IncomeLevel>$150k                                                          6.917e+03
## IncomeLevel$100-150k                                                       6.886e+03
## IncomeLevel$20-50k                                                         6.910e+03
## IncomeLevel$50-100k                                                        6.909e+03
## BMI                                                                        6.890e+03
## CESD.10baseline                                                            6.923e+03
## SmokingStatusFormer Smoker                                                 6.919e+03
## SmokingStatusNever Smoked                                                  6.919e+03
## SmokingStatusOccasional Smoker                                             6.845e+03
## RelationshipstatusMarried                                                  6.943e+03
## RelationshipstatusSeparated                                                6.965e+03
## RelationshipstatusSingle                                                   6.942e+03
## RelationshipstatusWidowed                                                  6.916e+03
## LivingstatusAssisted Living                                                6.968e+03
## LivingstatusHouse                                                          6.923e+03
## LivingstatusOther                                                          6.788e+03
## AnxietyYes                                                                 6.900e+03
## MoodDisordYes                                                              6.916e+03
## Chronicconditions                                                          6.891e+03
## PASE_TOTALbaseline                                                         6.899e+03
## Animal_Fluency_Normedbaseline                                              6.896e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.802e+03
## timefactor2:Age_sexFemales 65+                                             6.850e+03
## timefactor2:Age_sexMales 45-64                                             6.847e+03
## timefactor2:Age_sexMales 65+                                               6.871e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.147e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.151e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.150e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.797e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.777e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.802e+03
##                                                                           t value
## (Intercept)                                                                14.462
## timefactor2                                                                 3.336
## PandemicFU2 data collected before COVID-19                                  2.508
## Age_sexFemales 65+                                                         -2.466
## Age_sexMales 45-64                                                          0.327
## Age_sexMales 65+                                                           -1.396
## EducationHigh School Diploma                                                1.793
## EducationLess than High School Diploma                                      0.341
## EducationSome College                                                       1.659
## EthnicityWhite                                                              3.096
## IncomeLevel>$150k                                                          -0.315
## IncomeLevel$100-150k                                                        0.378
## IncomeLevel$20-50k                                                         -1.803
## IncomeLevel$50-100k                                                         0.253
## BMI                                                                        -1.574
## CESD.10baseline                                                            -1.398
## SmokingStatusFormer Smoker                                                  1.718
## SmokingStatusNever Smoked                                                   1.229
## SmokingStatusOccasional Smoker                                              0.075
## RelationshipstatusMarried                                                   0.457
## RelationshipstatusSeparated                                                 0.958
## RelationshipstatusSingle                                                    0.462
## RelationshipstatusWidowed                                                  -1.006
## LivingstatusAssisted Living                                                -0.563
## LivingstatusHouse                                                           1.786
## LivingstatusOther                                                          -0.726
## AnxietyYes                                                                  0.417
## MoodDisordYes                                                               0.783
## Chronicconditions                                                          -1.102
## PASE_TOTALbaseline                                                          2.540
## Animal_Fluency_Normedbaseline                                              70.319
## timefactor2:PandemicFU2 data collected before COVID-19                     -0.893
## timefactor2:Age_sexFemales 65+                                             -2.158
## timefactor2:Age_sexMales 45-64                                             -1.803
## timefactor2:Age_sexMales 65+                                               -3.769
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.642
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -2.276
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.969
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -0.031
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.827
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.734
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.000854
## PandemicFU2 data collected before COVID-19                                0.012147
## Age_sexFemales 65+                                                        0.013674
## Age_sexMales 45-64                                                        0.743623
## Age_sexMales 65+                                                          0.162839
## EducationHigh School Diploma                                              0.073059
## EducationLess than High School Diploma                                    0.732763
## EducationSome College                                                     0.097242
## EthnicityWhite                                                            0.001971
## IncomeLevel>$150k                                                         0.753094
## IncomeLevel$100-150k                                                      0.705437
## IncomeLevel$20-50k                                                        0.071372
## IncomeLevel$50-100k                                                       0.800484
## BMI                                                                       0.115419
## CESD.10baseline                                                           0.162217
## SmokingStatusFormer Smoker                                                0.085893
## SmokingStatusNever Smoked                                                 0.219134
## SmokingStatusOccasional Smoker                                            0.940030
## RelationshipstatusMarried                                                 0.647853
## RelationshipstatusSeparated                                               0.338298
## RelationshipstatusSingle                                                  0.644070
## RelationshipstatusWidowed                                                 0.314660
## LivingstatusAssisted Living                                               0.573418
## LivingstatusHouse                                                         0.074067
## LivingstatusOther                                                         0.468167
## AnxietyYes                                                                0.676627
## MoodDisordYes                                                             0.433616
## Chronicconditions                                                         0.270453
## PASE_TOTALbaseline                                                        0.011119
## Animal_Fluency_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.371710
## timefactor2:Age_sexFemales 65+                                            0.030979
## timefactor2:Age_sexMales 45-64                                            0.071381
## timefactor2:Age_sexMales 65+                                              0.000165
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.521083
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.022887
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.332473
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.975367
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.067710
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.083039
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                *  
## Age_sexFemales 65+                                                        *  
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                             
## EducationHigh School Diploma                                              .  
## EducationLess than High School Diploma                                       
## EducationSome College                                                     .  
## EthnicityWhite                                                            ** 
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                        .  
## IncomeLevel$50-100k                                                          
## BMI                                                                          
## CESD.10baseline                                                              
## SmokingStatusFormer Smoker                                                .  
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         .  
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                            
## PASE_TOTALbaseline                                                        *  
## Animal_Fluency_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor2:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             *  
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 .  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelAnimals_adj11)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)
## timefactor                        7.7     7.7     1 6790.6    2.0084 0.1564758
## Pandemic                         45.4    45.4     1 6906.2   11.8625 0.0005762
## Age_sex                         151.3    50.4     3 6906.9   13.1735 1.421e-08
## Education                        20.1     6.7     3 6925.0    1.7521 0.1540613
## Ethnicity                        36.7    36.7     1 6856.8    9.5834 0.0019713
## IncomeLevel                      35.3     8.8     4 6905.0    2.3047 0.0559689
## BMI                               9.5     9.5     1 6890.3    2.4790 0.1154192
## CESD.10baseline                   7.5     7.5     1 6922.7    1.9539 0.1622167
## SmokingStatus                    14.5     4.8     3 6882.6    1.2625 0.2854374
## Relationshipstatus               14.2     3.5     4 6939.1    0.9240 0.4487816
## Livingstatus                     20.1     6.7     3 6891.0    1.7477 0.1549422
## Anxiety                           0.7     0.7     1 6900.2    0.1740 0.6766269
## MoodDisord                        2.3     2.3     1 6916.0    0.6132 0.4336158
## Chronicconditions                 4.7     4.7     1 6891.0    1.2146 0.2704535
## PASE_TOTALbaseline               24.7    24.7     1 6898.8    6.4497 0.0111190
## Animal_Fluency_Normedbaseline 18935.3 18935.3     1 6895.5 4944.7791 < 2.2e-16
## timefactor:Pandemic               2.5     2.5     1 6790.6    0.6563 0.4179072
## timefactor:Age_sex               77.3    25.8     3 6788.8    6.7277 0.0001577
## Pandemic:Age_sex                 17.5     5.8     3 6905.4    1.5204 0.2069983
## timefactor:Pandemic:Age_sex      21.8     7.3     3 6788.8    1.8946 0.1281639
##                                  
## timefactor                       
## Pandemic                      ***
## Age_sex                       ***
## Education                        
## Ethnicity                     ** 
## IncomeLevel                   .  
## BMI                              
## CESD.10baseline                  
## SmokingStatus                    
## Relationshipstatus               
## Livingstatus                     
## Anxiety                          
## MoodDisord                       
## Chronicconditions                
## PASE_TOTALbaseline            *  
## Animal_Fluency_Normedbaseline ***
## timefactor:Pandemic              
## timefactor:Age_sex            ***
## Pandemic:Age_sex                 
## timefactor:Pandemic:Age_sex      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.4.4.2) Estimated marginal means

lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.84813 0.1963363 7568.62 10.463258
##  FU2 data collected before COVID-19 11.12329 0.1959771 7489.15 10.739120
##  upper.CL
##  11.23301
##  11.50746
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  11.13461 0.1967266 7608.90 10.748972
##  FU2 data collected before COVID-19 11.30494 0.1960206 7493.67 10.920688
##  upper.CL
##  11.52025
##  11.68920
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.48842 0.2126232 8045.59 10.071627
##  FU2 data collected before COVID-19 10.88593 0.2129420 7939.42 10.468506
##  upper.CL
##  10.90522
##  11.30335
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.45396 0.2136275 8140.93 10.035198
##  FU2 data collected before COVID-19 10.74034 0.2133785 7981.20 10.322062
##  upper.CL
##  10.87273
##  11.15862
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.88301 0.1903029 7391.86 10.509961
##  FU2 data collected before COVID-19 10.79557 0.2021118 7787.57 10.399378
##  upper.CL
##  11.25606
##  11.19177
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.96815 0.1904624 7408.81 10.594787
##  FU2 data collected before COVID-19 11.08680 0.2020930 7785.72 10.690648
##  upper.CL
##  11.34151
##  11.48296
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.65914 0.2112242 7906.51 10.245089
##  FU2 data collected before COVID-19 10.75254 0.2121533 8016.36 10.336662
##  upper.CL
##  11.07320
##  11.16841
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  10.40974 0.2124336 8022.37  9.993316
##  FU2 data collected before COVID-19 10.74719 0.2123725 8037.50 10.330890
##  upper.CL
##  10.82617
##  11.16350
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2751588 0.1097019 11503.38  -2.508  0.0121
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1703334 0.1105891 11618.98  -1.540  0.1235
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3975046 0.1563074 11434.70  -2.543  0.0110
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.2863766 0.1583368 11621.75  -1.809  0.0705
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##   0.0874378 0.1159025 11505.83   0.754  0.4506
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.1186569 0.1160415 11523.25  -1.023  0.3065
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.0933932 0.1523238 11488.88  -0.613  0.5398
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df t.ratio p.value
##  -0.3374540 0.1541943 11663.88  -2.188  0.0287
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.2751588 0.1097019 11503.38 -0.4901933 -0.06012438
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.1703334 0.1105891 11618.98 -0.3871065  0.04643979
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.3975046 0.1563074 11434.70 -0.7038939 -0.09111533
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.2863766 0.1583368 11621.75 -0.5967433  0.02399020
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##   0.0874378 0.1159025 11505.83 -0.1397508  0.31462647
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.1186569 0.1160415 11523.25 -0.3461180  0.10880427
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.0933932 0.1523238 11488.88 -0.3919739  0.20518742
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE       df   lower.CL    upper.CL
##  -0.3374540 0.1541943 11663.88 -0.6397007 -0.03520741
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.4.4.3) Graph of estimated marginal means

Animals_lsmeans_adj11 <- summary(lsmeans(modelAnimals_adj11, ~timefactor|Pandemic|Age_sex))
Animals_lsmeans_adj11$Time<-NA
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

11.5) Physical Activity Results

11.5.1) Main Effects Model

11.5.1.1) Model

modelPASE_6<- lmer(PASE_TOTAL ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long_2)
summary(modelPASE_6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 34098.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6805 -0.5406 -0.0438  0.5107  4.5754 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1216     34.87   
##  Residual             2223     47.15   
## Number of obs: 3125, groups:  ID, 2543
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             232.73128   15.86106
## timefactor2                                             -19.62725    2.64845
## PandemicFU2 data collected before COVID-19               -5.30365    3.06354
## Age                                                      -2.36897    0.14780
## SexM                                                     18.17102    2.52075
## EducationHigh School Diploma                            -11.08048    3.74359
## EducationLess than High School Diploma                   -8.20565    6.08434
## EducationSome College                                     5.41348    4.24920
## EthnicityWhite                                           11.18200    7.07555
## IncomeLevel>$150k                                        11.54903    6.28360
## IncomeLevel$100-150k                                     13.26436    5.18297
## IncomeLevel$20-50k                                        6.06846    3.73566
## IncomeLevel$50-100k                                       9.01259    3.90698
## BMI                                                      -0.50866    0.26619
## CESD.10baseline                                           0.29557    0.29246
## SmokingStatusFormer Smoker                                4.30350    5.28360
## SmokingStatusNever Smoked                                 7.29594    5.46236
## SmokingStatusOccasional Smoker                            0.86889    9.28644
## RelationshipstatusMarried                                -3.04107    4.06275
## RelationshipstatusSeparated                               9.25215    8.04699
## RelationshipstatusSingle                                 -6.55354    5.61456
## RelationshipstatusWidowed                                -0.69439    5.87389
## LivingstatusAssisted Living                             -13.41829   17.10814
## LivingstatusHouse                                        13.36191    3.74568
## LivingstatusOther                                        37.92922   15.38442
## AnxietyYes                                               -3.58410    5.05293
## MoodDisordYes                                           -11.36014    3.55337
## Chronicconditions                                        -1.63359    0.57824
## PASE_TOTALbaseline                                        0.36106    0.01686
## timefactor2:PandemicFU2 data collected before COVID-19    9.13538    3.86024
##                                                                df t value
## (Intercept)                                            2433.40877  14.673
## timefactor2                                            1845.93403  -7.411
## PandemicFU2 data collected before COVID-19             3079.70469  -1.731
## Age                                                    2437.60274 -16.028
## SexM                                                   2423.85638   7.209
## EducationHigh School Diploma                           2429.98070  -2.960
## EducationLess than High School Diploma                 2440.54924  -1.349
## EducationSome College                                  2338.14352   1.274
## EthnicityWhite                                         2585.73345   1.580
## IncomeLevel>$150k                                      2381.36205   1.838
## IncomeLevel$100-150k                                   2437.66731   2.559
## IncomeLevel$20-50k                                     2373.86541   1.624
## IncomeLevel$50-100k                                    2376.39493   2.307
## BMI                                                    2435.29043  -1.911
## CESD.10baseline                                        2398.83981   1.011
## SmokingStatusFormer Smoker                             2359.64679   0.815
## SmokingStatusNever Smoked                              2358.57043   1.336
## SmokingStatusOccasional Smoker                         2330.59744   0.094
## RelationshipstatusMarried                              2463.12175  -0.749
## RelationshipstatusSeparated                            2362.19214   1.150
## RelationshipstatusSingle                               2355.45363  -1.167
## RelationshipstatusWidowed                              2401.80448  -0.118
## LivingstatusAssisted Living                            2279.55813  -0.784
## LivingstatusHouse                                      2423.26416   3.567
## LivingstatusOther                                      2414.59723   2.465
## AnxietyYes                                             2382.38859  -0.709
## MoodDisordYes                                          2437.12530  -3.197
## Chronicconditions                                      2361.78407  -2.825
## PASE_TOTALbaseline                                     2398.87470  21.419
## timefactor2:PandemicFU2 data collected before COVID-19 1763.51326   2.367
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.90e-13 ***
## PandemicFU2 data collected before COVID-19             0.083513 .  
## Age                                                     < 2e-16 ***
## SexM                                                   7.52e-13 ***
## EducationHigh School Diploma                           0.003108 ** 
## EducationLess than High School Diploma                 0.177574    
## EducationSome College                                  0.202790    
## EthnicityWhite                                         0.114144    
## IncomeLevel>$150k                                      0.066192 .  
## IncomeLevel$100-150k                                   0.010551 *  
## IncomeLevel$20-50k                                     0.104409    
## IncomeLevel$50-100k                                    0.021152 *  
## BMI                                                    0.056135 .  
## CESD.10baseline                                        0.312300    
## SmokingStatusFormer Smoker                             0.415439    
## SmokingStatusNever Smoked                              0.181784    
## SmokingStatusOccasional Smoker                         0.925462    
## RelationshipstatusMarried                              0.454215    
## RelationshipstatusSeparated                            0.250357    
## RelationshipstatusSingle                               0.243231    
## RelationshipstatusWidowed                              0.905906    
## LivingstatusAssisted Living                            0.432933    
## LivingstatusHouse                                      0.000368 ***
## LivingstatusOther                                      0.013754 *  
## AnxietyYes                                             0.478201    
## MoodDisordYes                                          0.001406 ** 
## Chronicconditions                                      0.004766 ** 
## PASE_TOTALbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.018063 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelPASE_6)
## Type III Analysis of Variance Table with Satterthwaite's method
##                      Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## timefactor           135140  135140     1 1762.0  60.7975 1.074e-14 ***
## Pandemic                230     230     1 2390.8   0.1036 0.7476220    
## Age                  571054  571054     1 2437.6 256.9083 < 2.2e-16 ***
## Sex                  115505  115505     1 2423.9  51.9637 7.519e-13 ***
## Education             28705    9568     3 2402.6   4.3047 0.0048985 ** 
## Ethnicity              5552    5552     1 2585.7   2.4976 0.1141438    
## IncomeLevel           17847    4462     4 2404.8   2.0073 0.0908788 .  
## BMI                    8117    8117     1 2435.3   3.6515 0.0561354 .  
## CESD.10baseline        2270    2270     1 2398.8   1.0214 0.3122999    
## SmokingStatus          6210    2070     3 2356.4   0.9313 0.4246854    
## Relationshipstatus     9599    2400     4 2365.6   1.0797 0.3648644    
## Livingstatus          39483   13161     3 2370.5   5.9209 0.0005071 ***
## Anxiety                1118    1118     1 2382.4   0.5031 0.4782013    
## MoodDisord            22719   22719     1 2437.1  10.2208 0.0014064 ** 
## Chronicconditions     17741   17741     1 2361.8   7.9813 0.0047660 ** 
## PASE_TOTALbaseline  1019776 1019776     1 2398.9 458.7810 < 2.2e-16 ***
## timefactor:Pandemic   12449   12449     1 1763.5   5.6005 0.0180633 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.5.1.2) Estimated marginal means

lsmeans(modelPASE_6, ~Pandemic|timefactor)
## timefactor = 1:
##  Pandemic                             lsmean       SE      df lower.CL upper.CL
##  FU2 data collected after COVID-19  162.9041 8.110944 2551.82 146.9994 178.8088
##  FU2 data collected before COVID-19 157.6005 8.158128 2566.09 141.6033 173.5977
## 
## timefactor = 2:
##  Pandemic                             lsmean       SE      df lower.CL upper.CL
##  FU2 data collected after COVID-19  143.2769 8.069597 2533.88 127.4532 159.1006
##  FU2 data collected before COVID-19 147.1086 8.128889 2552.01 131.1687 163.0485
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_6, ~Pandemic|timefactor), "pairwise", adj="none")
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df t.ratio p.value
##   5.303654 3.064859 3081.18   1.730  0.0836
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df t.ratio p.value
##  -3.831727 2.920903 3094.29  -1.312  0.1897
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelPASE_6, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df  lower.CL  upper.CL
##   5.303654 3.064859 3081.18 -0.705720 11.313028
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE      df  lower.CL  upper.CL
##  -3.831727 2.920903 3094.29 -9.558833  1.895378
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.5.1.3) Graph of estimated marginal means

PASE_lsmeans_6 <- summary(lsmeans(modelPASE_6, ~timefactor|Pandemic))
PASE_lsmeans_6$Time<-NA
PASE_lsmeans_6$Time[PASE_lsmeans_6$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_6$Time[PASE_lsmeans_6$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_6, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

11.5.2) Age and Sex Interaction Model

11.5.2.1) Model

modelPASE_8<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long_2)
summary(modelPASE_8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 34149.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5698 -0.5427 -0.0392  0.4979  4.0877 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1398     37.39   
##  Residual             2202     46.92   
## Number of obs: 3125, groups:  ID, 2543
## 
## Fixed effects:
##                                                                            Estimate
## (Intercept)                                                                 95.4964
## timefactor2                                                                -25.7490
## PandemicFU2 data collected before COVID-19                                  -8.8218
## Age_sexFemales 65+                                                         -35.2604
## Age_sexMales 45-64                                                          13.2060
## Age_sexMales 65+                                                           -18.0100
## EducationHigh School Diploma                                               -11.7484
## EducationLess than High School Diploma                                     -12.0805
## EducationSome College                                                        3.2351
## EthnicityWhite                                                               7.2713
## IncomeLevel>$150k                                                           12.7741
## IncomeLevel$100-150k                                                        14.9002
## IncomeLevel$20-50k                                                           4.6884
## IncomeLevel$50-100k                                                          9.2970
## BMI                                                                         -0.4072
## CESD.10baseline                                                              0.4619
## SmokingStatusFormer Smoker                                                   2.3891
## SmokingStatusNever Smoked                                                    6.3021
## SmokingStatusOccasional Smoker                                               3.0490
## RelationshipstatusMarried                                                   -1.8200
## RelationshipstatusSeparated                                                 11.7951
## RelationshipstatusSingle                                                    -3.8715
## RelationshipstatusWidowed                                                   -9.8022
## LivingstatusAssisted Living                                                -21.8178
## LivingstatusHouse                                                           15.5912
## LivingstatusOther                                                           41.5047
## AnxietyYes                                                                  -1.3635
## MoodDisordYes                                                              -11.0153
## Chronicconditions                                                           -2.5631
## PASE_TOTALbaseline                                                           0.4154
## timefactor2:PandemicFU2 data collected before COVID-19                      13.3645
## timefactor2:Age_sexFemales 65+                                              12.8453
## timefactor2:Age_sexMales 45-64                                              12.0538
## timefactor2:Age_sexMales 65+                                                -5.2585
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               11.3888
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                4.0639
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 -7.7593
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   -8.8063
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -14.4876
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     15.9599
##                                                                           Std. Error
## (Intercept)                                                                  13.2006
## timefactor2                                                                   4.6306
## PandemicFU2 data collected before COVID-19                                    5.1185
## Age_sexFemales 65+                                                            6.9763
## Age_sexMales 45-64                                                            4.8934
## Age_sexMales 65+                                                              6.2591
## EducationHigh School Diploma                                                  3.8518
## EducationLess than High School Diploma                                        6.2412
## EducationSome College                                                         4.3676
## EthnicityWhite                                                                7.2563
## IncomeLevel>$150k                                                             6.4611
## IncomeLevel$100-150k                                                          5.3259
## IncomeLevel$20-50k                                                            3.8403
## IncomeLevel$50-100k                                                           4.0208
## BMI                                                                           0.2734
## CESD.10baseline                                                               0.3006
## SmokingStatusFormer Smoker                                                    5.4325
## SmokingStatusNever Smoked                                                     5.6200
## SmokingStatusOccasional Smoker                                                9.5521
## RelationshipstatusMarried                                                     4.2028
## RelationshipstatusSeparated                                                   8.2761
## RelationshipstatusSingle                                                      5.7846
## RelationshipstatusWidowed                                                     6.0223
## LivingstatusAssisted Living                                                  17.6015
## LivingstatusHouse                                                             3.8523
## LivingstatusOther                                                            15.8426
## AnxietyYes                                                                    5.1909
## MoodDisordYes                                                                 3.6572
## Chronicconditions                                                             0.5931
## PASE_TOTALbaseline                                                            0.0166
## timefactor2:PandemicFU2 data collected before COVID-19                        6.3516
## timefactor2:Age_sexFemales 65+                                                8.8644
## timefactor2:Age_sexMales 45-64                                                6.2820
## timefactor2:Age_sexMales 65+                                                  8.3477
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                 9.2772
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                 7.6516
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                   9.2440
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    11.8532
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64     9.6114
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      11.8721
##                                                                                  df
## (Intercept)                                                               2550.9670
## timefactor2                                                               1661.3322
## PandemicFU2 data collected before COVID-19                                3047.3970
## Age_sexFemales 65+                                                        3084.8545
## Age_sexMales 45-64                                                        3083.8475
## Age_sexMales 65+                                                          3084.8802
## EducationHigh School Diploma                                              2448.8516
## EducationLess than High School Diploma                                    2454.2637
## EducationSome College                                                     2361.3771
## EthnicityWhite                                                            2585.0053
## IncomeLevel>$150k                                                         2402.6331
## IncomeLevel$100-150k                                                      2452.4249
## IncomeLevel$20-50k                                                        2389.6708
## IncomeLevel$50-100k                                                       2395.1996
## BMI                                                                       2452.6833
## CESD.10baseline                                                           2415.6237
## SmokingStatusFormer Smoker                                                2380.8832
## SmokingStatusNever Smoked                                                 2378.9700
## SmokingStatusOccasional Smoker                                            2356.0664
## RelationshipstatusMarried                                                 2479.2893
## RelationshipstatusSeparated                                               2382.8738
## RelationshipstatusSingle                                                  2380.2978
## RelationshipstatusWidowed                                                 2420.7702
## LivingstatusAssisted Living                                               2311.1037
## LivingstatusHouse                                                         2441.9050
## LivingstatusOther                                                         2437.9568
## AnxietyYes                                                                2402.2627
## MoodDisordYes                                                             2454.0552
## Chronicconditions                                                         2384.3109
## PASE_TOTALbaseline                                                        2420.2438
## timefactor2:PandemicFU2 data collected before COVID-19                    1587.7940
## timefactor2:Age_sexFemales 65+                                            1727.9138
## timefactor2:Age_sexMales 45-64                                            1769.4141
## timefactor2:Age_sexMales 65+                                              1847.3384
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             3066.5688
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             3057.8793
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               3065.8730
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1685.9006
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1693.7893
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   1826.7015
##                                                                           t value
## (Intercept)                                                                 7.234
## timefactor2                                                                -5.561
## PandemicFU2 data collected before COVID-19                                 -1.724
## Age_sexFemales 65+                                                         -5.054
## Age_sexMales 45-64                                                          2.699
## Age_sexMales 65+                                                           -2.877
## EducationHigh School Diploma                                               -3.050
## EducationLess than High School Diploma                                     -1.936
## EducationSome College                                                       0.741
## EthnicityWhite                                                              1.002
## IncomeLevel>$150k                                                           1.977
## IncomeLevel$100-150k                                                        2.798
## IncomeLevel$20-50k                                                          1.221
## IncomeLevel$50-100k                                                         2.312
## BMI                                                                        -1.489
## CESD.10baseline                                                             1.536
## SmokingStatusFormer Smoker                                                  0.440
## SmokingStatusNever Smoked                                                   1.121
## SmokingStatusOccasional Smoker                                              0.319
## RelationshipstatusMarried                                                  -0.433
## RelationshipstatusSeparated                                                 1.425
## RelationshipstatusSingle                                                   -0.669
## RelationshipstatusWidowed                                                  -1.628
## LivingstatusAssisted Living                                                -1.240
## LivingstatusHouse                                                           4.047
## LivingstatusOther                                                           2.620
## AnxietyYes                                                                 -0.263
## MoodDisordYes                                                              -3.012
## Chronicconditions                                                          -4.322
## PASE_TOTALbaseline                                                         25.029
## timefactor2:PandemicFU2 data collected before COVID-19                      2.104
## timefactor2:Age_sexFemales 65+                                              1.449
## timefactor2:Age_sexMales 45-64                                              1.919
## timefactor2:Age_sexMales 65+                                               -0.630
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.228
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.531
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.839
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -0.743
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.507
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.344
##                                                                           Pr(>|t|)
## (Intercept)                                                               6.16e-13
## timefactor2                                                               3.13e-08
## PandemicFU2 data collected before COVID-19                                 0.08490
## Age_sexFemales 65+                                                        4.57e-07
## Age_sexMales 45-64                                                         0.00700
## Age_sexMales 65+                                                           0.00404
## EducationHigh School Diploma                                               0.00231
## EducationLess than High School Diploma                                     0.05303
## EducationSome College                                                      0.45895
## EthnicityWhite                                                             0.31640
## IncomeLevel>$150k                                                          0.04815
## IncomeLevel$100-150k                                                       0.00519
## IncomeLevel$20-50k                                                         0.22227
## IncomeLevel$50-100k                                                        0.02085
## BMI                                                                        0.13659
## CESD.10baseline                                                            0.12456
## SmokingStatusFormer Smoker                                                 0.66013
## SmokingStatusNever Smoked                                                  0.26224
## SmokingStatusOccasional Smoker                                             0.74961
## RelationshipstatusMarried                                                  0.66503
## RelationshipstatusSeparated                                                0.15423
## RelationshipstatusSingle                                                   0.50339
## RelationshipstatusWidowed                                                  0.10373
## LivingstatusAssisted Living                                                0.21527
## LivingstatusHouse                                                         5.34e-05
## LivingstatusOther                                                          0.00885
## AnxietyYes                                                                 0.79283
## MoodDisordYes                                                              0.00262
## Chronicconditions                                                         1.61e-05
## PASE_TOTALbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.03553
## timefactor2:Age_sexFemales 65+                                             0.14749
## timefactor2:Age_sexMales 45-64                                             0.05517
## timefactor2:Age_sexMales 65+                                               0.52882
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.21969
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.59538
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.40132
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.45761
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.13191
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.17901
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                .  
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ** 
## Age_sexMales 65+                                                          ** 
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                    .  
## EducationSome College                                                        
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                         *  
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       *  
## BMI                                                                          
## CESD.10baseline                                                              
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         ** 
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                         ***
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    *  
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
anova(modelPASE_8)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## timefactor                   109000  109000     1 1780.4  49.5072 2.809e-12 ***
## Pandemic                        443     443     1 2436.6   0.2013  0.653724    
## Age_sex                      295635   98545     3 2437.3  44.7584 < 2.2e-16 ***
## Education                     29706    9902     3 2421.1   4.4974  0.003745 ** 
## Ethnicity                      2211    2211     1 2585.0   1.0042  0.316400    
## IncomeLevel                   23173    5793     4 2422.4   2.6312  0.032709 *  
## BMI                            4882    4882     1 2452.7   2.2174  0.136589    
## CESD.10baseline                5198    5198     1 2415.6   2.3607  0.124558    
## SmokingStatus                  6302    2101     3 2378.7   0.9540  0.413559    
## Relationshipstatus            14797    3699     4 2385.8   1.6802  0.151791    
## Livingstatus                  51639   17213     3 2395.1   7.8181 3.422e-05 ***
## Anxiety                         152     152     1 2402.3   0.0690  0.792831    
## MoodDisord                    19974   19974     1 2454.1   9.0721  0.002622 ** 
## Chronicconditions             41123   41123     1 2384.3  18.6779 1.611e-05 ***
## PASE_TOTALbaseline          1379272 1379272     1 2420.2 626.4547 < 2.2e-16 ***
## timefactor:Pandemic           15932   15932     1 1779.9   7.2360  0.007212 ** 
## timefactor:Age_sex             5099    1700     3 1762.0   0.7720  0.509628    
## Pandemic:Age_sex               4303    1434     3 2427.1   0.6515  0.581969    
## timefactor:Pandemic:Age_sex   14654    4885     3 1763.5   2.2186  0.084111 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

11.5.2.2) Estimated marginal means

lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex)
## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  167.0874  8.865896 2641.93 149.70262
##  FU2 data collected before COVID-19 158.2656  8.869613 2683.19 140.87368
##  upper.CL
##  184.4722
##  175.6576
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  141.3385  8.886581 2651.64 123.91313
##  FU2 data collected before COVID-19 145.8812  8.709964 2612.86 128.80211
##  upper.CL
##  158.7638
##  162.9604
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  131.8270  9.948158 2818.01 112.32058
##  FU2 data collected before COVID-19 134.3940  9.758285 2799.79 115.25990
##  upper.CL
##  151.3334
##  153.5282
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  118.9233  9.908784 2808.74  99.49411
##  FU2 data collected before COVID-19 126.0486  9.565317 2737.89 107.29265
##  upper.CL
##  138.3526
##  144.8046
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  180.2934  8.738755 2634.61 163.15789
##  FU2 data collected before COVID-19 175.5355  9.325321 2755.58 157.25019
##  upper.CL
##  197.4289
##  193.8208
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  166.5983  8.710159 2624.42 149.51877
##  FU2 data collected before COVID-19 160.7173  9.216441 2716.88 142.64538
##  upper.CL
##  183.6777
##  178.7893
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  149.0774  9.652428 2737.31 130.15060
##  FU2 data collected before COVID-19 132.4963 10.066341 2844.55 112.75821
##  upper.CL
##  168.0042
##  152.2343
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE      df  lower.CL
##  FU2 data collected after COVID-19  118.0700 10.001046 2833.96  98.45989
##  FU2 data collected before COVID-19 130.8133  9.607708 2719.54 111.97417
##  upper.CL
##  137.6800
##  149.6525
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##    8.821770 5.120799 3050.08   1.723  0.0850
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   -4.542776 4.784672 3082.88  -0.949  0.3425
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   -2.567071 7.750241 3074.31  -0.331  0.7405
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   -7.125282 7.747956 3074.54  -0.920  0.3578
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##    4.757892 5.703698 3066.22   0.834  0.4042
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##    5.880923 5.439540 3082.55   1.081  0.2797
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##   16.581112 7.724798 3073.13   2.146  0.0319
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df t.ratio p.value
##  -12.743366 7.620373 3078.17  -1.672  0.0946
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger
confint(contrast(lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##    8.821770 5.120799 3050.08  -1.218796 18.86234
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##   -4.542776 4.784672 3082.88 -13.924244  4.83869
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##   -2.567071 7.750241 3074.31 -17.763246 12.62910
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##   -7.125282 7.747956 3074.54 -22.316977  8.06641
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##    4.757892 5.703698 3066.22  -6.425564 15.94135
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##    5.880923 5.439540 3082.55  -4.784567 16.54641
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##   16.581112 7.724798 3073.13   1.434821 31.72740
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE      df   lower.CL upper.CL
##  -12.743366 7.620373 3078.17 -27.684897  2.19817
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95

11.5.2.3) Graph of estimated marginal means

PASE_lsmeans_8 <- summary(lsmeans(modelPASE_8, ~timefactor|Pandemic|Age_sex))
PASE_lsmeans_8$Time<-NA
PASE_lsmeans_8$Time[PASE_lsmeans_8$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_8$Time[PASE_lsmeans_8$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_8, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

11.6) Sedentary behaviour Results

11.6.1) Full model adjusted for baseline

Create binary variable for 4+ hrs/day of SB

truncated.data_long_2$SB.binary <- as.factor(ifelse(truncated.data_long_2$PASE_Sit==10, 1, 0))
truncated.data_long_2$SBbaseline.binary <- as.factor(ifelse(truncated.data_long_2$PASE_Sitbaseline==10, 1, 0))

11.6.1.1) Model

sit3 <- glmer(
  SB.binary ~ timefactor*Pandemic + Age + Sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 5.82512 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
summary(sit3)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic + Age + Sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: truncated.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  21003.9  21243.5 -10470.9  20941.9    16789 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9448 -0.6491 -0.4613  0.8081  2.1137 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.7869   0.8871  
## Number of obs: 16820, groups:  ID, 8439
## 
## Fixed effects:
##                                                         Estimate Std. Error
## (Intercept)                                            -2.438878   0.258268
## timefactor2                                             0.493396   0.048725
## PandemicFU2 data collected before COVID-19              0.010547   0.055654
## Age                                                     0.018804   0.002459
## SexM                                                   -0.134070   0.045472
## SBbaseline.binary1                                      1.109194   0.044652
## EducationHigh School Diploma                            0.021478   0.063887
## EducationLess than High School Diploma                  0.048183   0.087736
## EducationSome College                                  -0.042918   0.079263
## EthnicityWhite                                         -0.168342   0.124737
## IncomeLevel>$150k                                      -0.187402   0.118916
## IncomeLevel$100-150k                                    0.062562   0.095040
## IncomeLevel$20-50k                                     -0.177606   0.062881
## IncomeLevel$50-100k                                    -0.236504   0.068148
## BMI                                                     0.042011   0.004253
## CESD.10baseline                                         0.015356   0.005099
## SmokingStatusFormer Smoker                             -0.337788   0.084483
## SmokingStatusNever Smoked                              -0.339522   0.088405
## SmokingStatusOccasional Smoker                         -0.282008   0.176691
## RelationshipstatusMarried                              -0.205458   0.075229
## RelationshipstatusSeparated                            -0.058363   0.145821
## RelationshipstatusSingle                                0.045686   0.102348
## RelationshipstatusWidowed                              -0.033516   0.102652
## LivingstatusAssisted Living                             0.100816   0.302641
## LivingstatusHouse                                      -0.283833   0.068029
## LivingstatusOther                                      -0.638775   0.245913
## AnxietyYes                                              0.010322   0.088049
## MoodDisordYes                                           0.078996   0.064075
## Chronicconditions                                       0.026770   0.010353
## timefactor2:PandemicFU2 data collected before COVID-19 -0.254407   0.071820
##                                                        z value Pr(>|z|)    
## (Intercept)                                             -9.443  < 2e-16 ***
## timefactor2                                             10.126  < 2e-16 ***
## PandemicFU2 data collected before COVID-19               0.190 0.849687    
## Age                                                      7.647 2.05e-14 ***
## SexM                                                    -2.948 0.003194 ** 
## SBbaseline.binary1                                      24.841  < 2e-16 ***
## EducationHigh School Diploma                             0.336 0.736730    
## EducationLess than High School Diploma                   0.549 0.582880    
## EducationSome College                                   -0.541 0.588186    
## EthnicityWhite                                          -1.350 0.177152    
## IncomeLevel>$150k                                       -1.576 0.115045    
## IncomeLevel$100-150k                                     0.658 0.510367    
## IncomeLevel$20-50k                                      -2.824 0.004736 ** 
## IncomeLevel$50-100k                                     -3.470 0.000520 ***
## BMI                                                      9.878  < 2e-16 ***
## CESD.10baseline                                          3.011 0.002602 ** 
## SmokingStatusFormer Smoker                              -3.998 6.38e-05 ***
## SmokingStatusNever Smoked                               -3.841 0.000123 ***
## SmokingStatusOccasional Smoker                          -1.596 0.110477    
## RelationshipstatusMarried                               -2.731 0.006313 ** 
## RelationshipstatusSeparated                             -0.400 0.688982    
## RelationshipstatusSingle                                 0.446 0.655321    
## RelationshipstatusWidowed                               -0.327 0.744044    
## LivingstatusAssisted Living                              0.333 0.739044    
## LivingstatusHouse                                       -4.172 3.02e-05 ***
## LivingstatusOther                                       -2.598 0.009389 ** 
## AnxietyYes                                               0.117 0.906678    
## MoodDisordYes                                            1.233 0.217626    
## Chronicconditions                                        2.586 0.009720 ** 
## timefactor2:PandemicFU2 data collected before COVID-19  -3.542 0.000397 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 5.82512 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sit3)
## Analysis of Variance Table
##                     npar Sum Sq Mean Sq  F value
## timefactor             1  87.52   87.52  87.5240
## Pandemic               1   1.74    1.74   1.7378
## Age                    1 152.68  152.68 152.6803
## Sex                    1  27.20   27.20  27.1956
## SBbaseline.binary      1 851.02  851.02 851.0195
## Education              3   8.74    2.91   2.9149
## Ethnicity              1   1.72    1.72   1.7156
## IncomeLevel            4  27.28    6.82   6.8190
## BMI                    1 126.98  126.98 126.9839
## CESD.10baseline        1  25.19   25.19  25.1915
## SmokingStatus          3  24.29    8.10   8.0969
## Relationshipstatus     4  32.08    8.02   8.0209
## Livingstatus           3  23.99    8.00   7.9982
## Anxiety                1   0.44    0.44   0.4426
## MoodDisord             1   2.28    2.28   2.2754
## Chronicconditions      1   7.22    7.22   7.2168
## timefactor:Pandemic    1  13.79   13.79  13.7926

11.6.1.2) Predicted probabilities

ggpredict(sit3, c("timefactor", "Pandemic"))
## # Predicted probabilities of SB.binary
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.50 | [0.41, 0.58]
## 2          |      0.62 | [0.53, 0.70]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.50 | [0.42, 0.59]
## 2          |      0.56 | [0.47, 0.64]
## 
## Adjusted for:
## *                Age =                    60.00
## *                Sex =                        F
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.50
## *    CESD.10baseline =                     4.88
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.71
## *                 ID =     0 (population-level)

11.6.1.3) Contrasts for predicted probilities

Calculating estimated mean differences between cohorts

#Create data frame
sit.test.3 <- as.data.frame(ggpredict(sit3, c("timefactor", "Pandemic")))

#Determine standard errors
sit.test.3$standard.error <- (sit.test.3$predicted - sit.test.3$conf.low)/1.96

#Calculating estimated mean differences between cohorts at FU1
mean.diff.1b<-(subset(sit.test.3,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test.3,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.1b<-(sqrt(((subset(sit.test.3,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sit.test.3,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.1b<-mean.diff.1b + se.1b*1.96
LL.1b<-mean.diff.1b - se.1b*1.96


#Calculating estimated mean differences between cohorts at FU2
mean.diff.2b<-(subset(sit.test.3,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test.3,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.2b<-(sqrt(((subset(sit.test.3,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sit.test.3,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.2b<-mean.diff.2b + se.2b*1.96
LL.2b<-mean.diff.2b - se.2b*1.96


#Calculating z-scores for differences
z.1b<- (subset(sit.test.3,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test.3,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test.3,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
     (subset(sit.test.3,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))
     
z.2b<- (subset(sit.test.3,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test.3,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test.3,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sit.test.3,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))


#p-values for z-scores
p.1b<-2*pnorm(z.1b, mean = 0, sd = 1, lower.tail = TRUE)
p.2b<-2*pnorm(z.2b, mean = 0, sd = 1, lower.tail = FALSE)

Estimated mean differences between cohorts at FU1

mean.diff.1b #mean difference
## [1] -0.002636852
LL.1b #Lower 95% CI
## [1] -0.08824947
UL.1b #Upper 95% CI
## [1] 0.08297577
z.1b # z-score
## [1] -0.06036761
p.1b #p-value
## [1] 0.9518629

Estimated mean differences between cohorts at FU2

mean.diff.2b #mean difference
## [1] 0.05892594
LL.2b #Lower 95% CI
## [1] -0.02633159
UL.2b #Upper 95% CI
## [1] 0.1441835
z.2b # z-score
## [1] 1.354659
p.2b #p-value
## [1] 0.1755264

11.6.1.4) Graph of predicted probabilities

ggpredict(sit3, c("timefactor", "Pandemic")) %>% plot()

11.6.2) Age and Sex Interaction Model

11.6.2.1) Model

sit4 <- glmer(
  SB.binary ~ timefactor*Pandemic*Age_sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.229753 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
summary(sit4)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic * Age_sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: truncated.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  21016.8  21333.8 -10467.4  20934.8    16779 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9718 -0.6435 -0.4581  0.8060  2.0959 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.805    0.8972  
## Number of obs: 16820, groups:  ID, 8439
## 
## Fixed effects:
##                                                                            Estimate
## (Intercept)                                                               -1.571070
## timefactor2                                                                0.572651
## PandemicFU2 data collected before COVID-19                                -0.018589
## Age_sexFemales 65+                                                         0.440114
## Age_sexMales 45-64                                                        -0.147491
## Age_sexMales 65+                                                           0.329222
## SBbaseline.binary1                                                         1.149375
## EducationHigh School Diploma                                               0.025033
## EducationLess than High School Diploma                                     0.070873
## EducationSome College                                                     -0.034202
## EthnicityWhite                                                            -0.114631
## IncomeLevel>$150k                                                         -0.216194
## IncomeLevel$100-150k                                                       0.019222
## IncomeLevel$20-50k                                                        -0.178927
## IncomeLevel$50-100k                                                       -0.243763
## BMI                                                                        0.041106
## CESD.10baseline                                                            0.013798
## SmokingStatusFormer Smoker                                                -0.290818
## SmokingStatusNever Smoked                                                 -0.287367
## SmokingStatusOccasional Smoker                                            -0.286338
## RelationshipstatusMarried                                                 -0.188483
## RelationshipstatusSeparated                                               -0.015126
## RelationshipstatusSingle                                                   0.079845
## RelationshipstatusWidowed                                                  0.056238
## LivingstatusAssisted Living                                                0.066064
## LivingstatusHouse                                                         -0.278263
## LivingstatusOther                                                         -0.647783
## AnxietyYes                                                                -0.018822
## MoodDisordYes                                                              0.084557
## Chronicconditions                                                          0.031625
## timefactor2:PandemicFU2 data collected before COVID-19                    -0.150875
## timefactor2:Age_sexFemales 65+                                            -0.150989
## timefactor2:Age_sexMales 45-64                                            -0.052530
## timefactor2:Age_sexMales 65+                                              -0.119286
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -0.034580
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.189015
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.085252
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.156160
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.171533
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -0.371615
##                                                                           Std. Error
## (Intercept)                                                                 0.216976
## timefactor2                                                                 0.090819
## PandemicFU2 data collected before COVID-19                                  0.096868
## Age_sexFemales 65+                                                          0.121366
## Age_sexMales 45-64                                                          0.093900
## Age_sexMales 65+                                                            0.117037
## SBbaseline.binary1                                                          0.045266
## EducationHigh School Diploma                                                0.064224
## EducationLess than High School Diploma                                      0.088279
## EducationSome College                                                       0.079658
## EthnicityWhite                                                              0.125243
## IncomeLevel>$150k                                                           0.119436
## IncomeLevel$100-150k                                                        0.095267
## IncomeLevel$20-50k                                                          0.063285
## IncomeLevel$50-100k                                                         0.068466
## BMI                                                                         0.004263
## CESD.10baseline                                                             0.005115
## SmokingStatusFormer Smoker                                                  0.084721
## SmokingStatusNever Smoked                                                   0.088793
## SmokingStatusOccasional Smoker                                              0.177745
## RelationshipstatusMarried                                                   0.075681
## RelationshipstatusSeparated                                                 0.146241
## RelationshipstatusSingle                                                    0.102814
## RelationshipstatusWidowed                                                   0.102772
## LivingstatusAssisted Living                                                 0.303093
## LivingstatusHouse                                                           0.068229
## LivingstatusOther                                                           0.247369
## AnxietyYes                                                                  0.088465
## MoodDisordYes                                                               0.064462
## Chronicconditions                                                           0.010290
## timefactor2:PandemicFU2 data collected before COVID-19                      0.125297
## timefactor2:Age_sexFemales 65+                                              0.152266
## timefactor2:Age_sexMales 45-64                                              0.119069
## timefactor2:Age_sexMales 65+                                                0.149587
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.161640
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.141501
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.162893
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.210902
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.183505
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     0.212871
##                                                                           z value
## (Intercept)                                                                -7.241
## timefactor2                                                                 6.305
## PandemicFU2 data collected before COVID-19                                 -0.192
## Age_sexFemales 65+                                                          3.626
## Age_sexMales 45-64                                                         -1.571
## Age_sexMales 65+                                                            2.813
## SBbaseline.binary1                                                         25.392
## EducationHigh School Diploma                                                0.390
## EducationLess than High School Diploma                                      0.803
## EducationSome College                                                      -0.429
## EthnicityWhite                                                             -0.915
## IncomeLevel>$150k                                                          -1.810
## IncomeLevel$100-150k                                                        0.202
## IncomeLevel$20-50k                                                         -2.827
## IncomeLevel$50-100k                                                        -3.560
## BMI                                                                         9.642
## CESD.10baseline                                                             2.698
## SmokingStatusFormer Smoker                                                 -3.433
## SmokingStatusNever Smoked                                                  -3.236
## SmokingStatusOccasional Smoker                                             -1.611
## RelationshipstatusMarried                                                  -2.491
## RelationshipstatusSeparated                                                -0.103
## RelationshipstatusSingle                                                    0.777
## RelationshipstatusWidowed                                                   0.547
## LivingstatusAssisted Living                                                 0.218
## LivingstatusHouse                                                          -4.078
## LivingstatusOther                                                          -2.619
## AnxietyYes                                                                 -0.213
## MoodDisordYes                                                               1.312
## Chronicconditions                                                           3.073
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.204
## timefactor2:Age_sexFemales 65+                                             -0.992
## timefactor2:Age_sexMales 45-64                                             -0.441
## timefactor2:Age_sexMales 65+                                               -0.797
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -0.214
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               1.336
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.523
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -0.740
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.935
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -1.746
##                                                                           Pr(>|z|)
## (Intercept)                                                               4.46e-13
## timefactor2                                                               2.87e-10
## PandemicFU2 data collected before COVID-19                                0.847819
## Age_sexFemales 65+                                                        0.000287
## Age_sexMales 45-64                                                        0.116246
## Age_sexMales 65+                                                          0.004909
## SBbaseline.binary1                                                         < 2e-16
## EducationHigh School Diploma                                              0.696699
## EducationLess than High School Diploma                                    0.422073
## EducationSome College                                                     0.667660
## EthnicityWhite                                                            0.360051
## IncomeLevel>$150k                                                         0.070276
## IncomeLevel$100-150k                                                      0.840093
## IncomeLevel$20-50k                                                        0.004694
## IncomeLevel$50-100k                                                       0.000370
## BMI                                                                        < 2e-16
## CESD.10baseline                                                           0.006982
## SmokingStatusFormer Smoker                                                0.000598
## SmokingStatusNever Smoked                                                 0.001211
## SmokingStatusOccasional Smoker                                            0.107191
## RelationshipstatusMarried                                                 0.012756
## RelationshipstatusSeparated                                               0.917623
## RelationshipstatusSingle                                                  0.437398
## RelationshipstatusWidowed                                                 0.584230
## LivingstatusAssisted Living                                               0.827455
## LivingstatusHouse                                                         4.54e-05
## LivingstatusOther                                                         0.008827
## AnxietyYes                                                                0.831509
## MoodDisordYes                                                             0.189614
## Chronicconditions                                                         0.002116
## timefactor2:PandemicFU2 data collected before COVID-19                    0.228538
## timefactor2:Age_sexFemales 65+                                            0.321386
## timefactor2:Age_sexMales 45-64                                            0.659090
## timefactor2:Age_sexMales 65+                                              0.425200
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.830598
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.181620
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.600723
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.459035
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.349912
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.080858
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                          ** 
## SBbaseline.binary1                                                        ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                         .  
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                        ** 
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ***
## CESD.10baseline                                                           ** 
## SmokingStatusFormer Smoker                                                ***
## SmokingStatusNever Smoked                                                 ** 
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 *  
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         ** 
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         ** 
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 0.229753 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sit4)
## Analysis of Variance Table
##                             npar Sum Sq Mean Sq  F value
## timefactor                     1  84.26   84.26  84.2628
## Pandemic                       1   1.51    1.51   1.5111
## Age_sex                        3 144.16   48.05  48.0524
## SBbaseline.binary              1 903.68  903.68 903.6824
## Education                      3  11.77    3.92   3.9243
## Ethnicity                      1   0.67    0.67   0.6660
## IncomeLevel                    4  25.54    6.38   6.3839
## BMI                            1 121.99  121.99 121.9928
## CESD.10baseline                1  21.54   21.54  21.5443
## SmokingStatus                  3  17.64    5.88   5.8787
## Relationshipstatus             4  37.02    9.26   9.2551
## Livingstatus                   3  23.66    7.89   7.8866
## Anxiety                        1   0.11    0.11   0.1150
## MoodDisord                     1   2.52    2.52   2.5174
## Chronicconditions              1  10.30   10.30  10.3003
## timefactor:Pandemic            1  17.98   17.98  17.9819
## timefactor:Age_sex             3  10.78    3.59   3.5941
## Pandemic:Age_sex               3   4.44    1.48   1.4791
## timefactor:Pandemic:Age_sex    3   3.44    1.15   1.1466

11.6.2.2) Predicted probabilities

ggpredict(sit4, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of SB.binary
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.43 | [0.34, 0.52]
## FU2 data collected before COVID-19 |      0.42 | [0.34, 0.51]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.57 | [0.48, 0.66]
## FU2 data collected before COVID-19 |      0.53 | [0.44, 0.62]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.54 | [0.44, 0.63]
## FU2 data collected before COVID-19 |      0.52 | [0.43, 0.62]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.64 | [0.55, 0.72]
## FU2 data collected before COVID-19 |      0.55 | [0.46, 0.64]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.39 | [0.31, 0.48]
## FU2 data collected before COVID-19 |      0.43 | [0.34, 0.53]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.52 | [0.43, 0.61]
## FU2 data collected before COVID-19 |      0.48 | [0.39, 0.58]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.51 | [0.41, 0.61]
## FU2 data collected before COVID-19 |      0.53 | [0.43, 0.62]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.62 | [0.53, 0.71]
## FU2 data collected before COVID-19 |      0.51 | [0.41, 0.61]
## 
## Adjusted for:
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.50
## *    CESD.10baseline =                     4.88
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.71
## *                 ID =     0 (population-level)
sit.test4 <- as.data.frame(ggpredict(sit4, c("timefactor", "Pandemic", "Age_sex")))

sit.test4$standard.error <- (sit.test4$predicted - sit.test2$conf.low)/1.96

11.6.2.3) Contrasts for predicted probilities

Calculating mean differences and 95% CIs for each age/sex group

######### Females 45-64 years #########

#Calculating means and standard errors for females 45-64 years
mean.diff.Females.Young.1b<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                               subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)

se.Females.Young.1b<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                              (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.1b<-mean.diff.Females.Young.1b + se.Females.Young.1b*1.96
LL.Females.Young.1b<-mean.diff.Females.Young.1b - se.Females.Young.1b*1.96

mean.diff.Females.Young.2b<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                               subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)

se.Females.Young.2b<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                              (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.2b<-mean.diff.Females.Young.2b + se.Females.Young.2b*1.96
LL.Females.Young.2b<-mean.diff.Females.Young.2b - se.Females.Young.2b*1.96

#z-scores for females 45-64 years
z.Females.Young.1b <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                                                                                                                                                 (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

z.Females.Young.2b <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
                                                                                                                                                 (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

#p-values for females 45-64 years
p.Females.Young.1b<-2*pnorm(z.Females.Young.1b, mean = 0, sd = 1, lower.tail = FALSE)
p.Females.Young.2b<-2*pnorm(z.Females.Young.2b, mean = 0, sd = 1, lower.tail = FALSE)


########### Females 65+ years ###########

#Calculating means and standard errors for females  65+ years
mean.diff.Females.Old.1b<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                             subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)

se.Females.Old.1b<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                            (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.1b<-mean.diff.Females.Old.1b + se.Females.Old.1b*1.96
LL.Females.Old.1b<-mean.diff.Females.Old.1b - se.Females.Old.1b*1.96

mean.diff.Females.Old.2b<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                             subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)

se.Females.Old.2b<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                            (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.2b<-mean.diff.Females.Old.2b + se.Females.Old.2b*1.96
LL.Females.Old.2b<-mean.diff.Females.Old.2b - se.Females.Old.2b*1.96

#z-scores for females 65+ years
z.Females.Old.1b <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                       subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                                                                                                                                             (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

z.Females.Old.2b <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                       subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
                                                                                                                                             (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

#p-values for females 65+ years
p.Females.Old.1b<-2*pnorm(z.Females.Old.1b, mean = 0, sd = 1, lower.tail = FALSE)
p.Females.Old.2b<-2*pnorm(z.Females.Old.2b, mean = 0, sd = 1, lower.tail = FALSE)



########## Males 45-64 years ###########

#Calculating means and standard errors for males 45-64 years
mean.diff.Males.Young.1b<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                             subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)

se.Males.Young.1b<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                            (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.1b<-mean.diff.Males.Young.1b + se.Males.Young.1b*1.96
LL.Males.Young.1b<-mean.diff.Males.Young.1b - se.Males.Young.1b*1.96

mean.diff.Males.Young.2b<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                             subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)

se.Males.Young.2b<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                            (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.2b<-mean.diff.Males.Young.2b + se.Males.Young.2b*1.96
LL.Males.Young.2b<-mean.diff.Males.Young.2b - se.Males.Young.2b*1.96

#z-scores for males 45-64 years
z.Males.Young.1b <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                       subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                                                                                                                                             (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

z.Males.Young.2b <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                       subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
                                                                                                                                             (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

#p-values for males 45-64 years
p.Males.Young.1b<-2*pnorm(z.Males.Young.1b, mean = 0, sd = 1, lower.tail = TRUE)
p.Males.Young.2b<-2*pnorm(z.Males.Young.2b, mean = 0, sd = 1, lower.tail = FALSE)


########## Males 65+ years ###########

#Calculating means and standard errors for males 65+ years
mean.diff.Males.Old.1b<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                           subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)

se.Males.Old.1b<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                          (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.1b<-mean.diff.Males.Old.1b + se.Males.Old.1b*1.96
LL.Males.Old.1b<-mean.diff.Males.Old.1b - se.Males.Old.1b*1.96

mean.diff.Males.Old.2b<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                           subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)

se.Males.Old.2b<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                          (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.2b<-mean.diff.Males.Old.2b + se.Males.Old.2b*1.96
LL.Males.Old.2b<-mean.diff.Males.Old.2b - se.Males.Old.2b*1.96

#z-scores for males 65+ years
z.Males.Old.1b <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                     subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                                                                                                                                         (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

z.Males.Old.2b <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                     subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
                                                                                                                                         (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

#p-values for males 65+ years
p.Males.Old.1b<-2*pnorm(z.Males.Old.1b, mean = 0, sd = 1, lower.tail = TRUE)
p.Males.Old.2b<-2*pnorm(z.Males.Old.2b, mean = 0, sd = 1, lower.tail = FALSE)

Mean differences and 95% CIs for females 45-64 years at FU1

mean.diff.Females.Young.1b #Estimated mean difference
## [1] 0.004546467
LL.Females.Young.1b #LL CI
## [1] -0.08423877
UL.Females.Young.1b #UL CI
## [1] 0.0933317
z.Females.Young.1b #z-score
## [1] 0.1003666
p.Females.Young.1b #p-value
## [1] 0.9200533

Mean differences and 95% CIs for females 45-64 years at FU2

mean.diff.Females.Young.2b #Estimated mean difference
## [1] 0.04191875
LL.Females.Young.2b #LL CI
## [1] -0.07325256
UL.Females.Young.2b #UL CI
## [1] 0.1570901
z.Females.Young.2b #z-score
## [1] 0.7133787
p.Females.Young.2b #p-value
## [1] 0.4756115

Mean differences and 95% CIs for females 65+ years at FU1

mean.diff.Females.Old.1b #Estimated mean difference
## [1] 0.01323892
LL.Females.Old.1b #LL CI
## [1] -0.09328601
UL.Females.Old.1b #UL CI
## [1] 0.1197639
z.Females.Old.1b #z-score
## [1] 0.2435889
p.Females.Old.1b #p-value
## [1] 0.8075493

Mean differences and 95% CIs for females 65+ years at FU2

mean.diff.Females.Old.2b #Estimated mean difference
## [1] 0.08643343
LL.Females.Old.2b #LL CI
## [1] -0.03988814
UL.Females.Old.2b #UL CI
## [1] 0.212755
z.Females.Old.2b #z-score
## [1] 1.341097
p.Females.Old.2b #p-value
## [1] 0.1798888

Mean differences and 95% CIs for males 45-64 years at FU1

mean.diff.Males.Young.1b #Estimated mean difference
## [1] -0.04130713
LL.Males.Young.1b #LL CI
## [1] -0.1386132
UL.Males.Young.1b #UL CI
## [1] 0.05599889
z.Males.Young.1b #z-score
## [1] -0.8320346
p.Males.Young.1b #p-value
## [1] 0.4053894

Mean differences and 95% CIs for males 45-64 years at FU2

mean.diff.Males.Young.2b #Estimated mean difference
## [1] 0.03797661
LL.Males.Young.2b #LL CI
## [1] -0.07270224
UL.Males.Young.2b #UL CI
## [1] 0.1486555
z.Males.Young.2b #z-score
## [1] 0.6725238
p.Males.Young.2b #p-value
## [1] 0.5012503

Mean differences and 95% CIs for males 65+ years at FU1

mean.diff.Males.Old.1b #Estimated mean difference
## [1] -0.01664062
LL.Males.Old.1b #LL CI
## [1] -0.1212239
UL.Males.Old.1b #UL CI
## [1] 0.08794264
z.Males.Old.1b #z-score
## [1] -0.3118626
p.Males.Old.1b #p-value
## [1] 0.7551449

Mean differences and 95% CIs for males 65+ years at FU2

mean.diff.Males.Old.2b #Estimated mean difference
## [1] 0.1114892
LL.Males.Old.2b #LL CI
## [1] -0.01651658
UL.Males.Old.2b #UL CI
## [1] 0.239495
z.Males.Old.2b #z-score
## [1] 1.707101
p.Males.Old.2b #p-value
## [1] 0.08780323

11.6.2.4) Graph of predicted probabilities

ggpredict(sit4, c("timefactor","Pandemic","Age_sex")) %>% plot()

11.7) Sleep Results

11.7.1) Main Effects Model

11.7.1.1) Model

sleep3 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic + Age + Sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 3.52462 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
summary(sleep3)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## RSTLS_Sleep ~ timefactor * Pandemic + Age + Sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: truncated.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  20204.6  20444.9 -10071.3  20142.6    17150 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8930 -0.5724 -0.4216  0.8171  2.1927 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.8109   0.9005  
## Number of obs: 17181, groups:  ID, 8611
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                            -1.0418064  0.2628173
## timefactor2                                            -0.1945906  0.0492208
## PandemicFU2 data collected before COVID-19             -0.0515297  0.0561784
## Age                                                    -0.0119102  0.0025237
## SexM                                                   -0.2589129  0.0466107
## RSTLS_Sleepbaseline                                     0.9497744  0.0494642
## EducationHigh School Diploma                            0.1890673  0.0647808
## EducationLess than High School Diploma                  0.2068022  0.0893990
## EducationSome College                                   0.1085545  0.0811110
## EthnicityWhite                                          0.1331178  0.1304591
## IncomeLevel>$150k                                      -0.0381059  0.1220403
## IncomeLevel$100-150k                                    0.0865390  0.0975760
## IncomeLevel$20-50k                                     -0.0187249  0.0638439
## IncomeLevel$50-100k                                     0.0363318  0.0691827
## BMI                                                    -0.0003967  0.0042367
## CESD.10baseline                                         0.0632756  0.0056964
## SmokingStatusFormer Smoker                              0.0443048  0.0871885
## SmokingStatusNever Smoked                              -0.0658098  0.0912093
## SmokingStatusOccasional Smoker                         -0.0235334  0.1785804
## RelationshipstatusMarried                              -0.0057746  0.0767875
## RelationshipstatusSeparated                            -0.4337311  0.1529156
## RelationshipstatusSingle                               -0.1615102  0.1053283
## RelationshipstatusWidowed                              -0.1852212  0.1061766
## LivingstatusAssisted Living                             0.1545573  0.3106840
## LivingstatusHouse                                       0.1093385  0.0708444
## LivingstatusOther                                       0.0254967  0.2456618
## AnxietyYes                                             -0.1289796  0.0889902
## MoodDisordYes                                          -0.0165015  0.0648312
## Chronicconditions                                       0.0909348  0.0105718
## timefactor2:PandemicFU2 data collected before COVID-19  0.0442628  0.0737136
##                                                        z value Pr(>|z|)    
## (Intercept)                                             -3.964 7.37e-05 ***
## timefactor2                                             -3.953 7.70e-05 ***
## PandemicFU2 data collected before COVID-19              -0.917  0.35901    
## Age                                                     -4.719 2.37e-06 ***
## SexM                                                    -5.555 2.78e-08 ***
## RSTLS_Sleepbaseline                                     19.201  < 2e-16 ***
## EducationHigh School Diploma                             2.919  0.00352 ** 
## EducationLess than High School Diploma                   2.313  0.02071 *  
## EducationSome College                                    1.338  0.18078    
## EthnicityWhite                                           1.020  0.30755    
## IncomeLevel>$150k                                       -0.312  0.75486    
## IncomeLevel$100-150k                                     0.887  0.37514    
## IncomeLevel$20-50k                                      -0.293  0.76930    
## IncomeLevel$50-100k                                      0.525  0.59947    
## BMI                                                     -0.094  0.92540    
## CESD.10baseline                                         11.108  < 2e-16 ***
## SmokingStatusFormer Smoker                               0.508  0.61135    
## SmokingStatusNever Smoked                               -0.722  0.47059    
## SmokingStatusOccasional Smoker                          -0.132  0.89516    
## RelationshipstatusMarried                               -0.075  0.94005    
## RelationshipstatusSeparated                             -2.836  0.00456 ** 
## RelationshipstatusSingle                                -1.533  0.12518    
## RelationshipstatusWidowed                               -1.744  0.08108 .  
## LivingstatusAssisted Living                              0.497  0.61885    
## LivingstatusHouse                                        1.543  0.12274    
## LivingstatusOther                                        0.104  0.91734    
## AnxietyYes                                              -1.449  0.14723    
## MoodDisordYes                                           -0.255  0.79909    
## Chronicconditions                                        8.602  < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19   0.600  0.54819    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 3.52462 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sleep3)
## Analysis of Variance Table
##                     npar Sum Sq Mean Sq  F value
## timefactor             1  17.70   17.70  17.7039
## Pandemic               1   0.20    0.20   0.2009
## Age                    1  11.48   11.48  11.4777
## Sex                    1  75.90   75.90  75.8980
## RSTLS_Sleepbaseline    1 842.13  842.13 842.1342
## Education              3  19.37    6.46   6.4563
## Ethnicity              1   0.53    0.53   0.5317
## IncomeLevel            4   4.48    1.12   1.1196
## BMI                    1   5.89    5.89   5.8914
## CESD.10baseline        1 162.10  162.10 162.0981
## SmokingStatus          3   6.25    2.08   2.0850
## Relationshipstatus     4  17.59    4.40   4.3981
## Livingstatus           3   2.10    0.70   0.7013
## Anxiety                1   0.65    0.65   0.6518
## MoodDisord             1   0.31    0.31   0.3053
## Chronicconditions      1  80.04   80.04  80.0425
## timefactor:Pandemic    1   0.39    0.39   0.3871

11.7.1.2) Predicted probabilities

ggpredict(sleep3, c("timefactor", "Pandemic"))
## # Predicted probabilities of RSTLS_Sleep
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.29 | [0.22, 0.37]
## 2          |      0.25 | [0.19, 0.32]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.28 | [0.21, 0.36]
## 2          |      0.25 | [0.19, 0.32]
## 
## Adjusted for:
## *                 Age =                    60.00
## *                 Sex =                        F
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.51
## *     CESD.10baseline =                     4.90
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.71
## *                  ID =     0 (population-level)

11.7.1.3) Estimated Mean Differences Between Pre- and Post-pandemic Cohorts at FU1 and FU2

Calculating estimated mean differences between cohorts

#Create data frame
sleep.test.3 <- as.data.frame(ggpredict(sleep3, c("timefactor", "Pandemic")))

#Determine standard errors
sleep.test.3$standard.error <- (sleep.test.3$predicted - sleep.test.3$conf.low)/1.96

#Calculating estimated mean differences between cohorts at FU1
mean.diff.1c<-(subset(sleep.test.3,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test.3,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.1c<-(sqrt(((subset(sleep.test.3,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sleep.test.3,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.1c<-mean.diff.1c + se.1c*1.96
LL.1c<-mean.diff.1c - se.1c*1.96


#Calculating estimated mean differences between cohorts at FU2
mean.diff.2c<-(subset(sleep.test.3,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test.3,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.2c<-(sqrt(((subset(sleep.test.3,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sleep.test.3,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))

UL.2c<-mean.diff.2c + se.2c*1.96
LL.2c<-mean.diff.2c - se.2c*1.96


#Calculating z-scores for differences
z.1c<- (subset(sleep.test.3,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test.3,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test.3,x==1 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
     (subset(sleep.test.3,x==1 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))
     
z.2c<- (subset(sleep.test.3,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test.3,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test.3,x==2 & group == "FU2 data collected after COVID-19")$standard.error)^2 +
         (subset(sleep.test.3,x==2 & group == "FU2 data collected before COVID-19")$standard.error^2))/2))


#p-values for z-scores
p.1c<-2*pnorm(z.1c, mean = 0, sd = 1, lower.tail = FALSE)
p.2c<-2*pnorm(z.2c, mean = 0, sd = 1, lower.tail = FALSE)

Estimated mean differences between cohorts at FU1

mean.diff.1c #mean difference
## [1] 0.01047513
LL.1c #Lower 95% CI
## [1] -0.05599089
UL.1c #Upper 95% CI
## [1] 0.07694116
z.1c # z-score
## [1] 0.3088986
p.1c #p-value
## [1] 0.7573987

Estimated mean differences between cohorts at FU2

mean.diff.2c #mean difference
## [1] 0.001363091
LL.2c #Lower 95% CI
## [1] -0.05939068
UL.2c #Upper 95% CI
## [1] 0.06211686
z.2c # z-score
## [1] 0.04397518
p.2c #p-value
## [1] 0.9649242

11.7.1.4) Graph of predicted probabilities

ggpredict(sleep3, c("timefactor", "Pandemic")) %>% plot()

11.7.2) Full model adjusted for baseline (age and sex differences)

11.7.2.1) Model

sleep4 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic*Age_sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 4.71501 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
summary(sleep4)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: RSTLS_Sleep ~ timefactor * Pandemic * Age_sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: truncated.data_long_2
## 
##      AIC      BIC   logLik deviance df.resid 
##  20201.1  20518.9 -10059.6  20119.1    17140 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7458 -0.5726 -0.4185  0.8129  2.3670 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.8225   0.9069  
## Number of obs: 17181, groups:  ID, 8611
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                               -1.6343390
## timefactor2                                                               -0.2754714
## PandemicFU2 data collected before COVID-19                                -0.1104027
## Age_sexFemales 65+                                                        -0.2002653
## Age_sexMales 45-64                                                        -0.5172539
## Age_sexMales 65+                                                          -0.4712077
## RSTLS_Sleepbaseline                                                        0.9546104
## EducationHigh School Diploma                                               0.1797473
## EducationLess than High School Diploma                                     0.2136112
## EducationSome College                                                      0.1332032
## EthnicityWhite                                                             0.1308369
## IncomeLevel>$150k                                                         -0.0100607
## IncomeLevel$100-150k                                                       0.1190651
## IncomeLevel$20-50k                                                        -0.0225508
## IncomeLevel$50-100k                                                        0.0418234
## BMI                                                                        0.0009517
## CESD.10baseline                                                            0.0635611
## SmokingStatusFormer Smoker                                                 0.0340065
## SmokingStatusNever Smoked                                                 -0.0633799
## SmokingStatusOccasional Smoker                                            -0.0628344
## RelationshipstatusMarried                                                 -0.0255264
## RelationshipstatusSeparated                                               -0.4013104
## RelationshipstatusSingle                                                  -0.1510180
## RelationshipstatusWidowed                                                 -0.2338176
## LivingstatusAssisted Living                                                0.1620799
## LivingstatusHouse                                                          0.1285654
## LivingstatusOther                                                         -0.0917031
## AnxietyYes                                                                -0.1349351
## MoodDisordYes                                                             -0.0118410
## Chronicconditions                                                          0.0886673
## timefactor2:PandemicFU2 data collected before COVID-19                     0.1061261
## timefactor2:Age_sexFemales 65+                                             0.0848093
## timefactor2:Age_sexMales 45-64                                             0.2520917
## timefactor2:Age_sexMales 65+                                              -0.1858191
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -0.2228146
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.3026941
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.0524889
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.1033567
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.2996806
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.3004752
##                                                                           Std. Error
## (Intercept)                                                                0.2210454
## timefactor2                                                                0.0890423
## PandemicFU2 data collected before COVID-19                                 0.0938208
## Age_sexFemales 65+                                                         0.1207097
## Age_sexMales 45-64                                                         0.0920181
## Age_sexMales 65+                                                           0.1180696
## RSTLS_Sleepbaseline                                                        0.0496756
## EducationHigh School Diploma                                               0.0650237
## EducationLess than High School Diploma                                     0.0897028
## EducationSome College                                                      0.0812029
## EthnicityWhite                                                             0.1311166
## IncomeLevel>$150k                                                          0.1223743
## IncomeLevel$100-150k                                                       0.0976225
## IncomeLevel$20-50k                                                         0.0641603
## IncomeLevel$50-100k                                                        0.0693653
## BMI                                                                        0.0042405
## CESD.10baseline                                                            0.0057076
## SmokingStatusFormer Smoker                                                 0.0873423
## SmokingStatusNever Smoked                                                  0.0915454
## SmokingStatusOccasional Smoker                                             0.1795149
## RelationshipstatusMarried                                                  0.0771087
## RelationshipstatusSeparated                                                0.1528963
## RelationshipstatusSingle                                                   0.1055444
## RelationshipstatusWidowed                                                  0.1062529
## LivingstatusAssisted Living                                                0.3096306
## LivingstatusHouse                                                          0.0710524
## LivingstatusOther                                                          0.2483096
## AnxietyYes                                                                 0.0892929
## MoodDisordYes                                                              0.0651042
## Chronicconditions                                                          0.0104751
## timefactor2:PandemicFU2 data collected before COVID-19                     0.1240349
## timefactor2:Age_sexFemales 65+                                             0.1526848
## timefactor2:Age_sexMales 45-64                                             0.1191701
## timefactor2:Age_sexMales 65+                                               0.1566489
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.1631892
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.1399319
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.1665816
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.2161345
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.1856695
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.2228573
##                                                                           z value
## (Intercept)                                                                -7.394
## timefactor2                                                                -3.094
## PandemicFU2 data collected before COVID-19                                 -1.177
## Age_sexFemales 65+                                                         -1.659
## Age_sexMales 45-64                                                         -5.621
## Age_sexMales 65+                                                           -3.991
## RSTLS_Sleepbaseline                                                        19.217
## EducationHigh School Diploma                                                2.764
## EducationLess than High School Diploma                                      2.381
## EducationSome College                                                       1.640
## EthnicityWhite                                                              0.998
## IncomeLevel>$150k                                                          -0.082
## IncomeLevel$100-150k                                                        1.220
## IncomeLevel$20-50k                                                         -0.351
## IncomeLevel$50-100k                                                         0.603
## BMI                                                                         0.224
## CESD.10baseline                                                            11.136
## SmokingStatusFormer Smoker                                                  0.389
## SmokingStatusNever Smoked                                                  -0.692
## SmokingStatusOccasional Smoker                                             -0.350
## RelationshipstatusMarried                                                  -0.331
## RelationshipstatusSeparated                                                -2.625
## RelationshipstatusSingle                                                   -1.431
## RelationshipstatusWidowed                                                  -2.201
## LivingstatusAssisted Living                                                 0.523
## LivingstatusHouse                                                           1.809
## LivingstatusOther                                                          -0.369
## AnxietyYes                                                                 -1.511
## MoodDisordYes                                                              -0.182
## Chronicconditions                                                           8.465
## timefactor2:PandemicFU2 data collected before COVID-19                      0.856
## timefactor2:Age_sexFemales 65+                                              0.555
## timefactor2:Age_sexMales 45-64                                              2.115
## timefactor2:Age_sexMales 65+                                               -1.186
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -1.365
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               2.163
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.315
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -0.478
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.614
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.348
##                                                                           Pr(>|z|)
## (Intercept)                                                               1.43e-13
## timefactor2                                                                0.00198
## PandemicFU2 data collected before COVID-19                                 0.23930
## Age_sexFemales 65+                                                         0.09710
## Age_sexMales 45-64                                                        1.90e-08
## Age_sexMales 65+                                                          6.58e-05
## RSTLS_Sleepbaseline                                                        < 2e-16
## EducationHigh School Diploma                                               0.00570
## EducationLess than High School Diploma                                     0.01725
## EducationSome College                                                      0.10093
## EthnicityWhite                                                             0.31834
## IncomeLevel>$150k                                                          0.93448
## IncomeLevel$100-150k                                                       0.22260
## IncomeLevel$20-50k                                                         0.72523
## IncomeLevel$50-100k                                                        0.54655
## BMI                                                                        0.82242
## CESD.10baseline                                                            < 2e-16
## SmokingStatusFormer Smoker                                                 0.69702
## SmokingStatusNever Smoked                                                  0.48873
## SmokingStatusOccasional Smoker                                             0.72632
## RelationshipstatusMarried                                                  0.74061
## RelationshipstatusSeparated                                                0.00867
## RelationshipstatusSingle                                                   0.15247
## RelationshipstatusWidowed                                                  0.02777
## LivingstatusAssisted Living                                                0.60065
## LivingstatusHouse                                                          0.07038
## LivingstatusOther                                                          0.71190
## AnxietyYes                                                                 0.13075
## MoodDisordYes                                                              0.85568
## Chronicconditions                                                          < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.39221
## timefactor2:Age_sexFemales 65+                                             0.57858
## timefactor2:Age_sexMales 45-64                                             0.03440
## timefactor2:Age_sexMales 65+                                               0.23554
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.17213
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.03053
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.75269
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.63250
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.10652
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.17757
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ** 
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                        .  
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## RSTLS_Sleepbaseline                                                       ***
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                    *  
## EducationSome College                                                        
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                          
## BMI                                                                          
## CESD.10baseline                                                           ***
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                               ** 
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                 *  
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         .  
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            *  
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             *  
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations)
## Model failed to converge with max|grad| = 4.71501 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## failure to converge in 10000 evaluations
anova(sleep4)
## Analysis of Variance Table
##                             npar Sum Sq Mean Sq  F value
## timefactor                     1  18.83   18.83  18.8313
## Pandemic                       1   0.04    0.04   0.0416
## Age_sex                        3  96.92   32.31  32.3061
## RSTLS_Sleepbaseline            1 836.59  836.59 836.5882
## Education                      3  21.43    7.14   7.1440
## Ethnicity                      1   0.63    0.63   0.6265
## IncomeLevel                    4   4.54    1.14   1.1357
## BMI                            1   7.06    7.06   7.0578
## CESD.10baseline                1 160.14  160.14 160.1377
## SmokingStatus                  3   5.26    1.75   1.7543
## Relationshipstatus             4  14.90    3.72   3.7240
## Livingstatus                   3   2.91    0.97   0.9696
## Anxiety                        1   0.82    0.82   0.8221
## MoodDisord                     1   0.34    0.34   0.3435
## Chronicconditions              1  77.96   77.96  77.9610
## timefactor:Pandemic            1   0.14    0.14   0.1430
## timefactor:Age_sex             3   3.62    1.21   1.2066
## Pandemic:Age_sex               3  14.86    4.95   4.9539
## timefactor:Pandemic:Age_sex    3   7.50    2.50   2.5001

11.7.2.2) Predicted probabilities

ggpredict(sleep4, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of RSTLS_Sleep
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.32 | [0.25, 0.41]
## FU2 data collected before COVID-19 |      0.30 | [0.23, 0.38]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.27 | [0.20, 0.34]
## FU2 data collected before COVID-19 |      0.26 | [0.20, 0.34]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.28 | [0.21, 0.37]
## FU2 data collected before COVID-19 |      0.22 | [0.16, 0.29]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.24 | [0.18, 0.32]
## FU2 data collected before COVID-19 |      0.19 | [0.13, 0.26]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.16, 0.29]
## FU2 data collected before COVID-19 |      0.26 | [0.19, 0.34]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.16, 0.29]
## FU2 data collected before COVID-19 |      0.22 | [0.16, 0.29]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.23 | [0.17, 0.31]
## FU2 data collected before COVID-19 |      0.22 | [0.16, 0.30]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.16 | [0.11, 0.22]
## FU2 data collected before COVID-19 |      0.21 | [0.15, 0.28]
## 
## Adjusted for:
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.51
## *     CESD.10baseline =                     4.90
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.71
## *                  ID =     0 (population-level)
sleep.test4 <- as.data.frame(ggpredict(sleep4, c("timefactor", "Pandemic", "Age_sex")))

sleep.test4$standard.error <- (sleep.test4$predicted - sleep.test4$conf.low)/1.96

11.7.2.3) Estimated mean differences between pre- and post-pandemic cohorts by age and sex

Calculating mean differences and 95% CIs for each age/sex group

######### Females 45-64 years #########

#Calculating means and standard errors for females 45-64 years
mean.diff.Females.Young.1d<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.1d<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.1d<-mean.diff.Females.Young.1d + se.Females.Young.1d*1.96
LL.Females.Young.1d<-mean.diff.Females.Young.1d - se.Females.Young.1d*1.96

mean.diff.Females.Young.2d<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.2d<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

UL.Females.Young.2d<-mean.diff.Females.Young.2d + se.Females.Young.2d*1.96
LL.Females.Young.2d<-mean.diff.Females.Young.2d - se.Females.Young.2d*1.96

#z-scores for females 45-64 years
z.Females.Young.1d <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))
     
z.Females.Young.2d <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$standard.error)^2 +
         (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$standard.error^2))/2))

#p-values for females 45-64 years
p.Females.Young.1d<-2*pnorm(z.Females.Young.1d, mean = 0, sd = 1, lower.tail = FALSE)
p.Females.Young.2d<-2*pnorm(z.Females.Young.2d, mean = 0, sd = 1, lower.tail = FALSE)


########### Females 65+ years ###########

#Calculating means and standard errors for females  65+ years
mean.diff.Females.Old.1d<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.1d<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.1d<-mean.diff.Females.Old.1d + se.Females.Old.1d*1.96
LL.Females.Old.1d<-mean.diff.Females.Old.1d - se.Females.Old.1d*1.96

mean.diff.Females.Old.2d<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.2d<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

UL.Females.Old.2d<-mean.diff.Females.Old.2d + se.Females.Old.2d*1.96
LL.Females.Old.2d<-mean.diff.Females.Old.2d - se.Females.Old.2d*1.96

#z-scores for females 65+ years
z.Females.Old.1d <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))
     
z.Females.Old.2d <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$standard.error)^2 +
         (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$standard.error^2))/2))

#p-values for females 65+ years
p.Females.Old.1d<-2*pnorm(z.Females.Old.1d, mean = 0, sd = 1, lower.tail = FALSE)
p.Females.Old.2d<-2*pnorm(z.Females.Old.2d, mean = 0, sd = 1, lower.tail = FALSE)



########## Males 45-64 years ###########

#Calculating means and standard errors for males 45-64 years
mean.diff.Males.Young.1d<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.1d<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.1d<-mean.diff.Males.Young.1a + se.Males.Young.1a*1.96
LL.Males.Young.1d<-mean.diff.Males.Young.1a - se.Males.Young.1a*1.96

mean.diff.Males.Young.2d<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.2d<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

UL.Males.Young.2d<-mean.diff.Males.Young.2d + se.Males.Young.2d*1.96
LL.Males.Young.2d<-mean.diff.Males.Young.2d - se.Males.Young.2d*1.96

#z-scores for males 45-64 years
z.Males.Young.1d <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))
     
z.Males.Young.2d <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$standard.error)^2 +
         (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$standard.error^2))/2))

#p-values for males 45-64 years
p.Males.Young.1d<-2*pnorm(z.Males.Young.1d, mean = 0, sd = 1, lower.tail = TRUE)
p.Males.Young.2d<-2*pnorm(z.Males.Young.2d, mean = 0, sd = 1, lower.tail = FALSE)


########## Males 65+ years ###########

#Calculating means and standard errors for males 65+ years
mean.diff.Males.Old.1d<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.1d<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.1d<-mean.diff.Males.Old.1a + se.Males.Old.1a*1.96
LL.Males.Old.1d<-mean.diff.Males.Old.1a - se.Males.Old.1a*1.96

mean.diff.Males.Old.2d<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.2d<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

UL.Males.Old.2d<-mean.diff.Males.Old.2d + se.Males.Old.2d*1.96
LL.Males.Old.2d<-mean.diff.Males.Old.2d - se.Males.Old.2d*1.96

#z-scores for males 65+ years
z.Males.Old.1d <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))
     
z.Males.Old.2d <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$standard.error)^2 +
         (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$standard.error^2))/2))

#p-values for males 65+ years
p.Males.Old.1d<-2*pnorm(z.Males.Old.1d, mean = 0, sd = 1, lower.tail = FALSE)
p.Males.Old.2d<-2*pnorm(z.Males.Old.2d, mean = 0, sd = 1, lower.tail = TRUE)

Mean differences and 95% CIs for females 45-64 years at FU1

mean.diff.Females.Young.1d #Estimated mean difference
## [1] 0.02362387
LL.Females.Young.1d #LL CI
## [1] -0.04968394
UL.Females.Young.1d #UL CI
## [1] 0.09693168
z.Females.Young.1d #z-score
## [1] 0.6316215
p.Females.Young.1d #p-value
## [1] 0.5276342

Mean differences and 95% CIs for females 45-64 years at FU2

mean.diff.Females.Young.2d #Estimated mean difference
## [1] 0.0008325664
LL.Females.Young.2d #LL CI
## [1] -0.06499317
UL.Females.Young.2d #UL CI
## [1] 0.0666583
z.Females.Young.2d #z-score
## [1] 0.02479015
p.Females.Young.2d #p-value
## [1] 0.9802223

Mean differences and 95% CIs for females 65+ years at FU1

mean.diff.Females.Old.1d #Estimated mean difference
## [1] 0.06207411
LL.Females.Old.1d #LL CI
## [1] -0.004442388
UL.Females.Old.1d #UL CI
## [1] 0.1285906
z.Females.Old.1d #z-score
## [1] 1.829099
p.Females.Old.1d #p-value
## [1] 0.06738479

Mean differences and 95% CIs for females 65+ years at FU2

mean.diff.Females.Old.2d #Estimated mean difference
## [1] 0.05562014
LL.Females.Old.2d #LL CI
## [1] -0.004139666
UL.Females.Old.2d #UL CI
## [1] 0.11538
z.Females.Old.2d #z-score
## [1] 1.824227
p.Females.Old.2d #p-value
## [1] 0.0681177

Mean differences and 95% CIs for males 45-64 years at FU1

mean.diff.Males.Young.1d #Estimated mean difference
## [1] -0.03485029
LL.Males.Young.1d #LL CI
## [1] -0.09638396
UL.Males.Young.1d #UL CI
## [1] 0.01894493
z.Males.Young.1d #z-score
## [1] -1.094877
p.Males.Young.1d #p-value
## [1] 0.2735705

Mean differences and 95% CIs for males 45-64 years at FU2

mean.diff.Males.Young.2d #Estimated mean difference
## [1] 0.0002144097
LL.Males.Young.2d #LL CI
## [1] -0.0577052
UL.Males.Young.2d #UL CI
## [1] 0.05813402
z.Males.Young.2d #z-score
## [1] 0.007255626
p.Males.Young.2d #p-value
## [1] 0.9942109

Mean differences and 95% CIs for males 65+ years at FU1

mean.diff.Males.Old.1d #Estimated mean difference
## [1] 0.01005995
LL.Males.Old.1d #LL CI
## [1] -0.05517548
UL.Males.Old.1d #UL CI
## [1] 0.06025669
z.Males.Old.1d #z-score
## [1] 0.3166005
p.Males.Old.1d #p-value
## [1] 0.7515467

Mean differences and 95% CIs for males 65+ years at FU2

mean.diff.Males.Old.2d #Estimated mean difference
## [1] -0.05197637
LL.Males.Old.2d #LL CI
## [1] -0.105612
UL.Males.Old.2d #UL CI
## [1] 0.001659243
z.Males.Old.2d #z-score
## [1] -1.899366
p.Males.Old.2d #p-value
## [1] 0.05751631

11.7.2.4) Graph of predicted probabilities

ggpredict(sleep4, c("timefactor","Pandemic","Age_sex")) %>% plot()